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CN119295060B - Predictive maintenance management method and system for industrial robot - Google Patents

Predictive maintenance management method and system for industrial robot Download PDF

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CN119295060B
CN119295060B CN202411833414.2A CN202411833414A CN119295060B CN 119295060 B CN119295060 B CN 119295060B CN 202411833414 A CN202411833414 A CN 202411833414A CN 119295060 B CN119295060 B CN 119295060B
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孙洲
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Tianjin Saiwei Industrial Technology Co ltd
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Abstract

本发明提供一种工业机器人预测性维护管理方法及系统,涉及设备维护管理技术领域,包括:获取工业机器人的多模态异构数据,进行智能预处理,得到第一预处理数据,对第一预处理数据进行跨模态关联融合,生成融合数据,提取互补冗余信息并构建语义特征图谱;构建健康状态预测模型,将语义特征图谱添加至健康状态预测模型中,通过线性变换映射至嵌入空间并提取时序信息,进行特征交互得到交互特征,通过前馈神经网络层进行变换和特征增强,得到健康状态概率分布;通过贝叶斯推理融合生成综合健康状态分布,结合检测模型,识别异常模式和关键转折点,生成最优维护策略并对工业机器人进行协同维护,将维护结果上传至服务器并保存当前状态。

The present invention provides an industrial robot predictive maintenance management method and system, which relates to the technical field of equipment maintenance management, and includes: acquiring multimodal heterogeneous data of the industrial robot, performing intelligent preprocessing to obtain first preprocessed data, performing cross-modal correlation fusion on the first preprocessed data to generate fused data, extracting complementary redundant information and constructing a semantic feature map; constructing a health status prediction model, adding the semantic feature map to the health status prediction model, mapping it to an embedding space through linear transformation and extracting time series information, performing feature interaction to obtain interactive features, performing transformation and feature enhancement through a feedforward neural network layer to obtain a health status probability distribution; generating a comprehensive health status distribution through Bayesian reasoning fusion, combining a detection model to identify abnormal patterns and key turning points, generating an optimal maintenance strategy and performing collaborative maintenance on the industrial robot, uploading the maintenance results to a server and saving the current state.

Description

Predictive maintenance management method and system for industrial robot
Technical Field
The invention relates to the technical field of equipment maintenance management, in particular to a predictive maintenance management method and system for an industrial robot.
Background
Industrial robots are widely applied to modern manufacturing industry, play a key role in automatic production and flexible manufacturing, however, faults and accidental stoppage of the industrial robots cause production efficiency reduction, product quality reduction and maintenance cost increase, and traditional industrial robot maintenance mainly depends on regular maintenance and post-maintenance, so that potential faults are difficult to discover and prevent in time, and the maintenance is not in time and has low efficiency;
in the traditional maintenance technology, the comprehensive evaluation and comprehensive modeling of the health state of the industrial robot are lacked, the accurate health state evaluation and fault prediction are difficult to realize, and meanwhile, the real-time state monitoring and data sharing mechanism is lacked, so that the remote monitoring and collaborative management of the industrial robot are difficult to realize;
Accordingly, there is a need for a solution to the problems of the prior art.
Disclosure of Invention
The embodiment of the invention provides a predictive maintenance management method and system for an industrial robot, which at least can solve part of problems in the prior art.
In a first aspect of an embodiment of the present invention, there is provided an industrial robot predictive maintenance management method, including:
Acquiring multi-modal heterogeneous data of an industrial robot in real time through a preset sensing network, performing intelligent preprocessing on the multi-modal heterogeneous data, combining blockchain right-determining synchronization and generating an countermeasure network enhancement equalization operation to obtain first preprocessed data, performing cross-modal correlation fusion on the first preprocessed data through a multi-modal graph convolutional neural network to generate fusion data, extracting complementary redundant information among the fusion data, and constructing a semantic feature map corresponding to the industrial robot;
Constructing a health state prediction model corresponding to an industrial robot comprising a plurality of subsystems based on a converter model, adding the semantic feature map as input into the health state prediction model, mapping the semantic feature map to an embedded space by an input embedding layer through linear transformation, extracting time sequence information, combining self-attention layers to perform feature interaction to obtain interaction features, and performing transformation and feature enhancement on the interaction features through a feedforward neural network layer to obtain health state probability distribution of each subsystem;
Based on the health state probability distribution, generating comprehensive health state distribution through Bayesian inference fusion, combining an abnormality detection model based on active learning, combining active exploration and uncertainty sampling to identify an abnormality mode and key turning points in the comprehensive health state distribution, generating an optimal maintenance strategy based on health state influence information and combining a reinforcement learning algorithm, carrying out collaborative maintenance on the industrial robot through a game theory method, and uploading a maintenance result to a server in real time and storing the current state of the industrial robot.
In an alternative embodiment of the present invention,
Acquiring multi-mode heterogeneous data of the industrial robot in real time through a preset sensing network, performing intelligent preprocessing on the multi-mode heterogeneous data, combining blockchain acknowledgement synchronization and generating countermeasure network enhancement equalization operation, and obtaining first preprocessed data comprises the following steps:
A plurality of sensor composition sensing networks of types are deployed in advance at key parts and joints of an industrial robot, and multi-modal heterogeneous data of the industrial robot are obtained in real time through the sensing networks, wherein the multi-modal heterogeneous data comprise acceleration, vibration, temperature, current and position information;
Carrying out intelligent preprocessing on the acquired multi-mode heterogeneous data, carrying out noise reduction and outlier rejection on the original data through wavelet transformation operation, mapping heterogeneous data with different sampling rates and time stamps onto the same time scale by combining a dynamic time warping algorithm to obtain aligned data, and carrying out normalization processing on the aligned data to generate standard data;
And carrying out data enhancement on the standard data through a depth generation countermeasure network, expanding the scale of the standard data, balancing data category distribution by combining an interpolation synthesis algorithm, carrying out data encryption through an asymmetric encryption algorithm, and carrying out synchronization and trusted sharing at multiple ends based on a distributed consensus mechanism of a blockchain to obtain the first preprocessing data.
In an alternative embodiment of the present invention,
Performing cross-modal correlation fusion on the first preprocessing data through a multi-modal graph convolutional neural network to generate fusion data, and extracting complementary redundant information among the fusion data and constructing a semantic feature map corresponding to the industrial robot comprises the following steps:
Mapping the first preprocessing data into a pre-initialized heterogeneous graph structure, wherein each sensor node is used as a node of the graph, the node attribute is a multidimensional time sequence feature vector of a current sensor, the edge is the association strength between the sensors, the edge weight is the correlation and the dependency relationship between the nodes, and the node feature and the edge weight matrix are initialized;
Applying a multi-modal graph convolutional neural network to the heterogeneous graph structure, respectively extracting local features in each mode through a cross-modal convolutional kernel and a convolutional kernel function corresponding to each mode, distributing importance weights for the local features in each mode by combining an attention mechanism, performing cross-modal correlation fusion on convolution results of a plurality of modes according to the importance weights to generate joint semantic representation of each node, stacking a plurality of multi-modal graph convolutional neural network layers and adaptively aggregating node features to generate cross-modal features;
For the cross-modal features, calculating a correlation matrix between different modes based on pearson correlation coefficients, carrying out thresholding treatment on the correlation matrix and generating a sparse correlation matrix, carrying out community division on the sparse correlation matrix by combining a graph clustering algorithm, clustering the cross-modal features into clusters, wherein features in each cluster represent a group of complementary redundant semantic information, carrying out importance analysis on the features in each cluster, identifying key complementary redundant features by combining a feature selection method based on a graph, and adding the key complementary redundant features into a pre-initialized redundant set;
And constructing a semantic feature map, filling an instance layer of the semantic feature map by using the key complementary redundant features based on the redundant set, definitely defining type layers and attribute values, introducing a time dimension, and establishing semantic association among instances to obtain the semantic feature map.
In an alternative embodiment of the present invention,
Constructing a health state prediction model corresponding to an industrial robot comprising a plurality of subsystems based on a converter model, adding the semantic feature map as input into the health state prediction model, mapping the semantic feature map to an embedded space by an input embedding layer through linear transformation, extracting time sequence information, combining self-attention layers to perform feature interaction to obtain interaction features, transforming and feature enhancing the interaction features through a feedforward neural network layer, and obtaining health state probability distribution of each subsystem comprises the following steps:
Based on a converter model, constructing an encoder-decoder structure, adding a semantic feature map corresponding to an industrial robot into the converter model, mapping nodes in the semantic feature map into low-dimensional dense vectors through a node embedding technology, reserving a graph structure, designing the structure of the encoder and the decoder, introducing a position coding mechanism, adding a position embedding vector into an input embedding layer, and obtaining a health state prediction model corresponding to each subsystem;
Adding the semantic feature map as input into the health state prediction model, mapping nodes in the map into a low-dimensional embedding space through linear transformation by an input embedding layer, aggregating neighbor node information by combining a map attention network, updating embedded representation of each node, adding corresponding time stamp information in each node embedding, constructing a time sequence embedding matrix, determining time sequence information corresponding to the current node based on the time sequence embedding matrix, encoding the semantic feature map embedded with the time sequence information, dividing the map into a plurality of sub-graphs, wherein each sub-graph corresponds to a subsystem of an industrial robot, and extracting features and interacting features of each sub-graph based on an attention mechanism to obtain the interactive features;
based on the interaction characteristics, extracting association modes among different subsystems, adding the interaction characteristics into the health state prediction model, recursively generating the health state of the next time step, combining the association modes, combining an attention mechanism to dynamically adjust the influence degree of the different subsystems on the current prediction, and repeating the prediction until the preset maximum iteration times are reached, so as to obtain the health state probability distribution corresponding to each subsystem.
In an alternative embodiment of the present invention,
The embedded representation of aggregating neighbor node information and updating each node in conjunction with the graph attention network is shown in the following formula:
;
Wherein, Represents the updated hidden state of node i, h i represents the hidden state of node i, K represents the number of attention headers, sigma represents the activation function,Indicating that the kth attention header is at time step t, the attention weight of node i to neighbor node j, W k indicates the weight matrix of the kth attention header, h j indicates the hidden state of node j,Representing the attention weight of the kth attention head to itself by node i at time step t, N (i) represents the set of neighbor nodes of node i.
In an alternative embodiment of the present invention,
Based on the health state probability distribution, generating comprehensive health state distribution through Bayesian inference fusion, combining an abnormality detection model based on active learning, combining active exploration and uncertainty sampling to identify an abnormality mode and key turning points in the comprehensive health state distribution, generating an optimal maintenance strategy based on health state influence information and combining a reinforcement learning algorithm to cooperatively maintain the industrial robot through a game theory method, and uploading a maintenance result to a server in real time and storing the current state of the industrial robot comprises the following steps:
the health state probability distribution of each subsystem is used as an independent random variable, a joint probability distribution model is established, priori knowledge is introduced to construct priori distribution probability, posterior probability distribution corresponding to each subsystem is calculated based on Bayesian theorem by combining the prior distribution probability and health state likelihood functions of the subsystems, and the posterior probability distribution is used as the comprehensive health state distribution;
Based on the comprehensive health state distribution, an abnormal detection model based on active learning is constructed, the comprehensive health state distribution is modeled through an unsupervised learning method, a sample with the maximum information amount is selected for marking according to an active exploration strategy, the uncertainty of a current sample is evaluated through edge probability, the sample with the highest uncertainty is repeatedly evaluated and selected, and a manual marking label is added;
Updating the abnormal detection model according to the re-labeled sample, adaptively adjusting decision boundaries and model super-parameters of the abnormal detection model by combining an incremental learning algorithm, detecting the comprehensive health state distribution according to the updated abnormal detection model, and determining drift trend and the key turning points of the current health state distribution by combining a drift detection mechanism;
the method comprises the steps of obtaining fault risks, task importance, maintenance cost and residual service life corresponding to industrial robots, recording the fault risks, task importance, maintenance cost and residual service life as health state influence information, taking the health state influence information as a state space, taking maintenance operation as an action space, defining state transition probability and rewarding functions, updating function values between the state and the action through a reinforcement learning algorithm, generating an optimal maintenance strategy, determining a maintenance rewarding mechanism and a cooperation strategy between each industrial robot by combining a game theory method, repeatedly optimizing, obtaining a global optimal maintenance decision, carrying out cooperative maintenance on the industrial robots, and uploading the state parameters of the industrial robots after maintenance to a server and storing.
In an alternative embodiment of the present invention,
Based on the Bayes theorem, the posterior probability distribution corresponding to each subsystem is calculated by combining the prior probability and the health state likelihood function of each subsystem, and the posterior probability distribution is shown as the following formula:
;
Wherein S g represents the health state of the g-th subsystem, O represents the comprehensive health state evidence observed, P (o|s g) represents the likelihood that the comprehensive health state evidence O is observed under the condition that the g-th subsystem is in the health state S g, P (S g |o) represents the posterior probability that the g-th subsystem is in the health state S g under the condition that the comprehensive health state evidence O, P (S g) represents the prior probability that the g-th subsystem is in the health state S g, P (o|s j) represents the likelihood that the comprehensive health state evidence O is observed under the condition that the j-th subsystem is in the health state S j, P (S j) represents the prior probability that the j-th subsystem is in the health state S j, and n represents the total number of subsystem states.
In a second aspect of the embodiment of the present invention, there is provided an industrial robot predictive maintenance management system, including:
The system comprises a first unit, a second unit and a third unit, wherein the first unit is used for acquiring multi-modal heterogeneous data of the industrial robot in real time through a preset sensing network, performing intelligent preprocessing on the multi-modal heterogeneous data, combining blockchain right-determining synchronization and generating an countermeasure network enhancement equalization operation to obtain first preprocessed data, performing cross-modal correlation fusion on the first preprocessed data through a multi-modal graph convolutional neural network to generate fusion data, extracting complementary redundant information among the fusion data and constructing a semantic feature map corresponding to the industrial robot;
The second unit is used for constructing a health state prediction model corresponding to the industrial robot comprising a plurality of subsystems based on the converter model, adding the semantic feature map into the health state prediction model as input, mapping the semantic feature map into an embedded space by an input embedding layer through linear transformation, extracting time sequence information, combining a self-attention layer to perform feature interaction to obtain interaction features, and performing transformation and feature enhancement on the interaction features through a feedforward neural network layer to obtain health state probability distribution of each subsystem;
The third unit is used for generating comprehensive health state distribution through Bayesian inference fusion based on the health state probability distribution, identifying an abnormal mode and key turning points in the comprehensive health state distribution by combining an abnormal detection model based on active learning and combining active exploration and uncertainty sampling, generating an optimal maintenance strategy by combining a reinforcement learning algorithm based on health state influence information, carrying out cooperative maintenance on the industrial robot through a game theory method, uploading a maintenance result to a server in real time, and storing the current state of the industrial robot.
In a third aspect of an embodiment of the present invention,
There is provided an electronic device including:
A processor;
A memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method described previously.
In a fourth aspect of an embodiment of the present invention,
There is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method as described above.
According to the invention, the data enhancement and equalization operation is carried out by adopting the generation countermeasure network, the problems of data sparseness and unbalance are effectively solved, a good foundation is laid for subsequent analysis and modeling, the multimodal graph convolutional neural network is utilized to carry out cross-modal association fusion on the preprocessed data, complementary and redundant information among different modality data is fully mined, high-level semantic association among modalities can be effectively captured, more comprehensive and compact fusion data representation is generated, the fusion is carried out by adopting a Bayesian inference method, comprehensive health state distribution is generated, priori knowledge and observation data can be effectively combined, more reliable and comprehensive health state estimation is obtained, reinforcement learning can be realized through continuous interaction with the environment, and a decision sequence with maximized long-term benefit is learned, meanwhile, the maintenance cost is remarkably reduced, the availability of the robot is improved, in a comprehensive way, the health management level of the industrial robot is remarkably improved, the service life of the industrial robot is prolonged to the maximum extent, the external shutdown time is reduced, the production efficiency is improved, and the operation cost is reduced.
Drawings
FIG. 1 is a flow chart of a predictive maintenance management method for an industrial robot according to an embodiment of the invention;
fig. 2 is a schematic structural diagram of an industrial robot predictive maintenance management system according to an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Fig. 1 is a flow chart of a predictive maintenance management method for an industrial robot according to an embodiment of the invention, as shown in fig. 1, the method includes:
S1, acquiring multi-modal heterogeneous data of an industrial robot in real time through a preset sensing network, performing intelligent preprocessing on the multi-modal heterogeneous data, combining blockchain right-determining synchronization and generating an countermeasure network enhancement equalization operation to obtain first preprocessed data, performing cross-modal correlation fusion on the first preprocessed data through a multi-modal graph convolutional neural network to generate fusion data, extracting complementary redundant information among the fusion data, and constructing a semantic feature map corresponding to the industrial robot;
The multi-modal heterogeneous data is data with different formats and characteristics from different sources, the blockchain right synchronization is that the blockchain technology is utilized to manage and synchronize ownership and access rights of the data, the generation of the countermeasure network enhancement balanced operation is a data enhancement technology, the generation of the countermeasure network is used to generate synthesized data, the training data set is expanded, the generalization capability of a model is improved, the multi-modal graph convolutional neural network is a neural network model for processing the multi-modal data, the data of different modalities is mapped to the same graph structure, the graph rolling operation is utilized to extract cross-modal characteristic representation, the cross-modal correlation fusion is that the correlation relationship among different modalities is mined in the multi-modal data, the cross-modal information is fused, the complementary redundant information is that the information carried by different modalities may have certain complementarity and redundancy in the multi-modal data, the semantic characteristic graph is a structural knowledge graph representation method, and a semantic graph is formed through the association among concepts of building equipment parts, fault modes, maintenance operations and the like.
In an alternative embodiment of the present invention,
Acquiring multi-mode heterogeneous data of the industrial robot in real time through a preset sensing network, performing intelligent preprocessing on the multi-mode heterogeneous data, combining blockchain acknowledgement synchronization and generating countermeasure network enhancement equalization operation, and obtaining first preprocessed data comprises the following steps:
A plurality of sensor composition sensing networks of types are deployed in advance at key parts and joints of an industrial robot, and multi-modal heterogeneous data of the industrial robot are obtained in real time through the sensing networks, wherein the multi-modal heterogeneous data comprise acceleration, vibration, temperature, current and position information;
Carrying out intelligent preprocessing on the acquired multi-mode heterogeneous data, carrying out noise reduction and outlier rejection on the original data through wavelet transformation operation, mapping heterogeneous data with different sampling rates and time stamps onto the same time scale by combining a dynamic time warping algorithm to obtain aligned data, and carrying out normalization processing on the aligned data to generate standard data;
And carrying out data enhancement on the standard data through a depth generation countermeasure network, expanding the scale of the standard data, balancing data category distribution by combining an interpolation synthesis algorithm, carrying out data encryption through an asymmetric encryption algorithm, and carrying out synchronization and trusted sharing at multiple ends based on a distributed consensus mechanism of a blockchain to obtain the first preprocessing data.
The sensing network is a distributed network system composed of a large number of sensor nodes and is used for collecting, processing and transmitting information of the physical world, the wavelet transformation operation is a time-frequency analysis tool, time domain signals can be converted into time-frequency domain representations, local characteristics of the signals are extracted, the dynamic time warping algorithm is a time sequence alignment method and is used for processing time sequence data under different time scales or speeds, the interpolation synthesis algorithm is a data filling and reconstruction method and is used for processing missing or sparse data, the asymmetric encryption algorithm is an encryption method and is used for conducting encryption and decryption operations by using different keys and is used for protecting confidentiality of the data, and the distributed consensus mechanism based on block chains is a method for achieving consensus in the distributed system and is realized through a block chain technology.
The method comprises the steps that multiple types of sensors, such as an acceleration sensor, a vibration sensor, a temperature sensor, a current sensor and a position sensor, are deployed in advance at key parts and joints of the industrial robot, such as a speed reducer, a motor and a bearing, and multi-mode heterogeneous data of the industrial robot are obtained in real time through a sensing network, wherein the multi-mode heterogeneous data comprise acceleration, vibration, temperature, current and position information;
Performing intelligent preprocessing on the acquired multi-modal heterogeneous data, performing noise reduction and outlier rejection on the original data by utilizing wavelet transformation operation, mapping heterogeneous data with different sampling rates and time stamps onto the same time scale by adopting a dynamic time warping algorithm, performing normalization processing on the aligned data, scaling the data of different modalities into the same numerical range, and eliminating dimension influence;
The method comprises the steps of carrying out data enhancement on standard data by utilizing a depth generation countermeasure network (GAN), expanding the scale of the standard data, constructing a GAN model, comprising a generator and a discriminator, continuously improving the capability of the generator for generating vivid data through countermeasure training of the generator and the discriminator, expanding the scale of a standard data set, carrying out class balance on the enhanced data by adopting an interpolation synthesis algorithm, generating new synthesized samples in a feature space by adopting the interpolation algorithm for fault classes with fewer samples, increasing the data quantity of sparse classes, reducing the number of the samples in a random undersampling mode for normal classes with more samples, and balancing the data distribution of each class;
Encrypting the balanced data by using an asymmetric encryption algorithm, protecting confidentiality and integrity of the data, generating a public key and a private key pair, using the public key for encrypting the data, using the private key for decrypting the data, encrypting the data, converting plaintext data into ciphertext, setting up a blockchain network comprising a plurality of participating nodes such as an industrial robot manufacturer, an equipment owner, a maintenance service provider and the like, designing and deploying an intelligent contract, prescribing rules and rights of data sharing such as data ownership, access control and a use range, adopting a proper consensus algorithm such as workload certification (PoW), rights and interests certification (PoS) and the like, achieving data synchronization and consistency consensus among the plurality of nodes, uploading the encrypted data to the blockchain network, and performing data synchronization among the nodes by a consensus mechanism to obtain first preprocessing data.
In the embodiment, the multi-type sensor forming sensing network is deployed at key parts and joints of the industrial robot, multi-mode heterogeneous data comprising acceleration, vibration, temperature, current, position information and the like are collected in real time, comprehensive sensing and monitoring of the running state of the robot are achieved, wavelet transformation is adopted to reduce noise and abnormal values of original data, high-frequency noise and abnormal points in the data are eliminated, the signal-to-noise ratio and reliability of the data are improved, depth generation is utilized to enhance standard data by the countermeasure network, the scale and diversity of the standard data are expanded, data equalization is achieved through sparse type oversampling and majority type undersampling, accuracy and reliability of fault diagnosis are improved, based on a distributed consensus mechanism of a block chain, data synchronization and credible sharing among a plurality of nodes are achieved, in conclusion, the embodiment achieves comprehensive sensing, quality improvement, enhancement expansion, safe sharing and intelligent application of the running data of the industrial robot, provides key data support for predictive maintenance management of the industrial robot, improves timeliness, accuracy and efficiency of maintenance, and reduces unexpected risk.
In an alternative embodiment of the present invention,
Performing cross-modal correlation fusion on the first preprocessing data through a multi-modal graph convolutional neural network to generate fusion data, and extracting complementary redundant information among the fusion data and constructing a semantic feature map corresponding to the industrial robot comprises the following steps:
Mapping the first preprocessing data into a pre-initialized heterogeneous graph structure, wherein each sensor node is used as a node of the graph, the node attribute is a multidimensional time sequence feature vector of a current sensor, the edge is the association strength between the sensors, the edge weight is the correlation and the dependency relationship between the nodes, and the node feature and the edge weight matrix are initialized;
Applying a multi-modal graph convolutional neural network to the heterogeneous graph structure, respectively extracting local features in each mode through a cross-modal convolutional kernel and a convolutional kernel function corresponding to each mode, distributing importance weights for the local features in each mode by combining an attention mechanism, performing cross-modal correlation fusion on convolution results of a plurality of modes according to the importance weights to generate joint semantic representation of each node, stacking a plurality of multi-modal graph convolutional neural network layers and adaptively aggregating node features to generate cross-modal features;
For the cross-modal features, calculating a correlation matrix between different modes based on pearson correlation coefficients, carrying out thresholding treatment on the correlation matrix and generating a sparse correlation matrix, carrying out community division on the sparse correlation matrix by combining a graph clustering algorithm, clustering the cross-modal features into clusters, wherein features in each cluster represent a group of complementary redundant semantic information, carrying out importance analysis on the features in each cluster, identifying key complementary redundant features by combining a feature selection method based on a graph, and adding the key complementary redundant features into a pre-initialized redundant set;
And constructing a semantic feature map, filling an instance layer of the semantic feature map by using the key complementary redundant features based on the redundant set, definitely defining type layers and attribute values, introducing a time dimension, and establishing semantic association among instances to obtain the semantic feature map.
The multi-dimensional time sequence feature vector refers to a series of time sequence features extracted from multi-modal heterogeneous data, each time step corresponds to one multi-dimensional feature vector, the edge weight matrix refers to a matrix representing connection relation strength among nodes in graph structure data, the cross-modal convolution kernel is a convolution kernel function used for multi-modal data fusion, feature data from different modalities can be processed simultaneously, the convolution kernel function is core operation in a convolution neural network and is used for extracting local features, the sparse correlation matrix is a sparse matrix representing correlation among variables, most elements are zero, only few elements represent significant correlation, the key complementary redundancy feature refers to complementarity and redundancy existing among the different modal features in the multi-modal data, the instance layer refers to a hierarchy representing specific examples in knowledge, and community division refers to a process of dividing the nodes into different communities or modules in a complex network.
Mapping the first preprocessing data into a pre-initialized heterogeneous graph structure, wherein each sensor node is used as one node of the graph, the attribute of the node is a multidimensional time sequence feature vector of the current sensor, the multidimensional time sequence feature vector comprises feature data acquired by the sensor at different time steps, edges in the graph represent the association strength between the sensors, the weight of the edges represents the correlation and the dependency relationship between the nodes, and a node feature matrix and an edge weight matrix are initialized;
Applying a multi-modal graph convolutional neural network on a heterogeneous graph structure, carrying out feature extraction and fusion on multi-modal data, designing a corresponding convolutional kernel function for each mode, extracting local features in the mode, distributing importance weights for the local features through an attention mechanism in a convolutional result of each mode, carrying out weighted fusion on the convolutional result of different modes based on the attention weights, generating joint semantic representation of each node, realizing effective integration of cross-mode information, stacking a plurality of multi-modal graph convolutional neural network layers, extracting and fusing advanced semantic features layer by layer, generating final cross-mode features by self-adaptively aggregating the feature representation of each node, and capturing deep association and complementary information among different modes;
Calculating the correlation among different modal features based on pearson correlation coefficients to obtain a correlation matrix, carrying out thresholding treatment on the correlation matrix, setting the correlation lower than a threshold value to zero, generating a sparse correlation matrix, carrying out community division on the sparse correlation matrix by combining graph clustering algorithms such as spectral clustering, louvain algorithm and the like, aggregating cross-modal features into different clusters, wherein the features in each cluster represent a group of complementary redundant semantic information, evaluating the importance of each feature in the cluster by a feature selection method based on a graph, selecting a plurality of features with highest score as key complementary redundant features according to the importance score, and adding the selected key complementary redundant features into a pre-initialized redundant set to form a redundant set containing multi-modal key features;
Filling an instance layer of a semantic feature map by using key complementary redundant features in a redundancy set, wherein each feature is used as an instance node, carrying attribute information such as a mode, a time step, a feature value and the like to which the feature belongs, clearly defining types and layers of the semantic feature map, such as a sensor type, a fault type, a component type and the like, establishing inheritance and inclusion relations among the types, defining an attribute and a value range of each type, such as a measuring range of the sensor, a sampling frequency, a severity of the fault and the like, perfecting attribute description of the instance node, introducing a time dimension into the semantic feature map, representing state changes of the instance node in different time steps, connecting the instance nodes in different time steps through time edges to form a time sequence relation, establishing semantic association edges such as a causal relation, a correlation relation, a synonymous relation and the like among the instance nodes according to domain knowledge and expert experience, enriching semantic expression capability of the map, and finally obtaining the semantic feature map.
In the embodiment, the multi-modal sensor data is mapped into the heterogeneous graph structure, the sensors are represented by nodes, the sensors are represented by edges, the graphical representation of the multi-modal data is realized, the extraction of local features and the fusion of cross-modal features are realized by cross-modal convolution kernels and modal specific convolution kernel functions, the complementary and redundant relations among different modalities are identified by computing correlation matrixes among the cross-modal features and performing sparsification processing and community division, the semantic feature patterns form structural knowledge representation in the industrial robot predictive maintenance field, semantic support is provided for intelligent fault diagnosis and maintenance decision, and in sum, the deep fusion and semantic enhancement of the industrial robot multi-modal sensor data are realized, the knowledge patterns facing predictive maintenance are constructed, and important technical support is provided for intelligent fault diagnosis, health assessment and maintenance policy optimization.
S2, constructing a health state prediction model corresponding to the industrial robot comprising a plurality of subsystems based on a converter model, adding the semantic feature map as input into the health state prediction model, mapping the semantic feature map to an embedded space by an input embedding layer through linear transformation, extracting time sequence information, combining a self-attention layer to perform feature interaction to obtain interaction features, and performing transformation and feature enhancement on the interaction features through a feedforward neural network layer to obtain health state probability distribution of each subsystem;
The converter model is a deep learning model based on a self-attention mechanism, and is generally applied to the fields of natural language processing, computer vision and the like, the health state prediction model is a machine learning model used for predicting the current and future health states of an industrial robot in predictive maintenance of the industrial robot, the input embedding layer is a layer for mapping discrete input tokens to continuous vector representation in the converter model, the embedding space is a continuous vector space to which the input embedding layer maps discrete tokens, and the interaction characteristics are interaction information between different positions calculated by the self-attention mechanism in the converter model.
In an alternative embodiment of the present invention,
Constructing a health state prediction model corresponding to an industrial robot comprising a plurality of subsystems based on a converter model, adding the semantic feature map as input into the health state prediction model, mapping the semantic feature map to an embedded space by an input embedding layer through linear transformation, extracting time sequence information, combining self-attention layers to perform feature interaction to obtain interaction features, transforming and feature enhancing the interaction features through a feedforward neural network layer, and obtaining health state probability distribution of each subsystem comprises the following steps:
Based on a converter model, constructing an encoder-decoder structure, adding a semantic feature map corresponding to an industrial robot into the converter model, mapping nodes in the semantic feature map into low-dimensional dense vectors through a node embedding technology, reserving a graph structure, designing the structure of the encoder and the decoder, introducing a position coding mechanism, adding a position embedding vector into an input embedding layer, and obtaining a health state prediction model corresponding to each subsystem;
Adding the semantic feature map as input into the health state prediction model, mapping nodes in the map into a low-dimensional embedding space through linear transformation by an input embedding layer, aggregating neighbor node information by combining a map attention network, updating embedded representation of each node, adding corresponding time stamp information in each node embedding, constructing a time sequence embedding matrix, determining time sequence information corresponding to the current node based on the time sequence embedding matrix, encoding the semantic feature map embedded with the time sequence information, dividing the map into a plurality of sub-graphs, wherein each sub-graph corresponds to a subsystem of an industrial robot, and extracting features and interacting features of each sub-graph based on an attention mechanism to obtain the interactive features;
based on the interaction characteristics, extracting association modes among different subsystems, adding the interaction characteristics into the health state prediction model, recursively generating the health state of the next time step, combining the association modes, combining an attention mechanism to dynamically adjust the influence degree of the different subsystems on the current prediction, and repeating the prediction until the preset maximum iteration times are reached, so as to obtain the health state probability distribution corresponding to each subsystem.
The position coding mechanism is to assign a unique position code to each position of the input sequence in the converter model to introduce position information, and the position embedding vector is to generate a unique vector representation for each position in the position coding mechanism.
On the basis of a converter model, constructing an encoder-decoder structure for predicting the health state of the industrial robot, wherein the encoder is responsible for encoding an input semantic feature map into a hidden representation, the decoder generates a prediction result of the health state according to the hidden representation, the semantic feature map corresponding to the industrial robot is added into the converter model, the semantic representation capability of the model is enhanced by utilizing node information and side information in the map, the nodes in the semantic feature map are mapped into low-dimensional dense vectors through a node embedding technology, meanwhile, map structure information is reserved, each node is represented as a vector with a fixed dimension, and each element in the vector represents the coordinates of the node in an embedding space;
Introducing a position coding mechanism into the structures of an encoder and a decoder to capture position information in an input sequence, adding a position embedded vector in an input embedded layer besides mapping a node into an embedded vector, adding or splicing the position embedded vector and the node embedded vector to obtain final input representation, constructing a corresponding health state prediction model for each subsystem of the industrial robot, wherein the model of each subsystem is based on a converter structure and comprises the encoder and the decoder, the encoder receives a semantic feature sub-graph corresponding to the subsystem as input, extracting features through a self-attention mechanism and a feedforward neural network, generating health state prediction of the next time step according to the output of the encoder, and obtaining a health state sequence in a future period of the subsystem through repeated iterative prediction;
Inputting semantic feature patterns into a health state prediction model, mapping pattern nodes into a low-dimensional embedding space through an input embedding layer, aggregating neighbor information of each node through a graph attention network, updating embedding representation of the nodes, adding corresponding timestamp information in the embedding representation of each node, constructing a time sequence embedding matrix, wherein each row of the time sequence embedding matrix represents embedding vectors of one node at different time steps, each column represents embedding vectors of different nodes at the same time step, encoding the semantic feature patterns of the embedding time sequence information, dividing the patterns into a plurality of sub-graphs, each sub-graph corresponds to one sub-system of the industrial robot, carrying out feature extraction and feature interaction on each sub-graph based on an attention mechanism, and calculating correlation and interaction strength among different sub-graphs through the attention mechanism to obtain interaction features representing a correlation mode among the sub-systems;
The extracted interaction characteristics are added into a health state prediction model to serve as additional input information, when the health state of the next time step is predicted, the node embedding and the historical health state of the current time step are utilized, the interaction characteristics are combined, the association mode among the subsystems is captured, the influence degree of different subsystems on the current prediction is dynamically adjusted, interaction among the subsystems can be fully considered by a prediction result, the health state of the next time step is recursively generated until the preset maximum iteration times are reached, and the health state probability distribution of each subsystem is obtained.
In the embodiment, the semantic feature map corresponding to the industrial robot is added to the converter model, the semantic representation capability of the model is enriched by utilizing node information and side information in the map, the node embedding technology maps the map nodes into low-dimensional dense vectors, meanwhile, map structure information is reserved, the model can fully utilize structured knowledge of the map, quality of feature representation and accuracy of prediction are improved, a position coding mechanism is introduced, the model can capture position information in an input sequence, node representations of different time steps are distinguished, feature extraction and feature interaction are carried out on each sub-map through an attention mechanism, interaction features representing a correlation mode among sub-systems are obtained, dependency and influence mechanisms among different sub-systems are fully considered, context information of a cross-subsystem is provided, global feature and correlation of health state prediction are enhanced, evolution rules of map structuring knowledge and time sequence data are fully utilized, correlation modes among sub-systems are captured, future predictive maintenance decision support is provided, and accurate prediction and comprehensive evaluation of the industrial robot health state are realized.
In an alternative embodiment of the present invention,
The embedded representation of aggregating neighbor node information and updating each node in conjunction with the graph attention network is shown in the following formula:
;
Wherein, Represents the updated hidden state of node i, h i represents the hidden state of node i, K represents the number of attention headers, sigma represents the activation function,Indicating that the kth attention header is at time step t, the attention weight of node i to neighbor node j, W k indicates the weight matrix of the kth attention header, h j indicates the hidden state of node j,Representing the attention weight of the kth attention head to itself by node i at time step t, N (i) represents the set of neighbor nodes of node i.
In this embodiment, different importance weights are allocated to neighbor nodes of each node through an attention mechanism, so that selective aggregation of neighbor information is achieved, an adaptive weight allocation mechanism enables a graph attention network to flexibly adjust contribution degrees of neighbor nodes to central node representation according to semantic similarity and structural relation of the nodes, semantic richness and distinguishability of node representation are improved, the graph attention network can capture diversified interaction modes among the nodes from different semantic subspaces through introducing a plurality of attention heads, information of different semantic subspaces can be synthesized, robustness and expression capability of representation are enhanced, hidden states of the nodes can be propagated and updated in a graph structure in multiple steps through iteratively applying the graph attention network, in this embodiment, local and global information of the graph structure are fully utilized, diversified interaction modes among the nodes are captured through the adaptive attention weight allocation and the attention mechanism, accuracy, robustness and reliability of industrial robot health state prediction can be effectively improved, and reliability of intelligent transportation and prediction are provided for maintenance and prediction.
S3, based on the health state probability distribution, generating comprehensive health state distribution through Bayesian inference fusion, combining an abnormality detection model based on active learning, combining active exploration and uncertainty sampling to identify an abnormality mode and key turning points in the comprehensive health state distribution, generating an optimal maintenance strategy based on health state influence information and combining a reinforcement learning algorithm, carrying out collaborative maintenance on the industrial robot through a game theory method, uploading a maintenance result to a server in real time, and storing the current state of the industrial robot.
The Bayesian inference is an inference method based on probability theory and statistics, the prior knowledge and observation data are fused, the probability distribution of unknown quantity is updated to obtain posterior probability distribution, the active exploration is a method for reducing uncertainty and optimizing decisions by actively selecting mobile phone information through behaviors, the uncertainty sampling is an active learning strategy, the sample with the largest uncertainty in model prediction is selected for labeling and learning, the generalization capability and the robustness of the model are improved, the key turning points are important time nodes in the evolution process of the health state of the industrial robot and are usually represented as abrupt change or abnormal fluctuation of the state, the game theory is a mathematical theory for researching strategy interaction and decision optimization among a plurality of participants, and in predictive maintenance of the industrial robot, the game theory method can be applied to collaborative decision and optimization among a plurality of stakeholders, and the collaborative maintenance refers to predictive maintenance tasks of the industrial robot are jointly completed through information sharing, resource coordination and task division.
In an alternative embodiment of the present invention,
Based on the health state probability distribution, generating comprehensive health state distribution through Bayesian inference fusion, combining an abnormality detection model based on active learning, combining active exploration and uncertainty sampling to identify an abnormality mode and key turning points in the comprehensive health state distribution, generating an optimal maintenance strategy based on health state influence information and combining a reinforcement learning algorithm to cooperatively maintain the industrial robot through a game theory method, and uploading a maintenance result to a server in real time and storing the current state of the industrial robot comprises the following steps:
the health state probability distribution of each subsystem is used as an independent random variable, a joint probability distribution model is established, priori knowledge is introduced to construct priori distribution probability, posterior probability distribution corresponding to each subsystem is calculated based on Bayesian theorem by combining the prior distribution probability and health state likelihood functions of the subsystems, and the posterior probability distribution is used as the comprehensive health state distribution;
Based on the comprehensive health state distribution, an abnormal detection model based on active learning is constructed, the comprehensive health state distribution is modeled through an unsupervised learning method, a sample with the maximum information amount is selected for marking according to an active exploration strategy, the uncertainty of a current sample is evaluated through edge probability, the sample with the highest uncertainty is repeatedly evaluated and selected, and a manual marking label is added;
Updating the abnormal detection model according to the re-labeled sample, adaptively adjusting decision boundaries and model super-parameters of the abnormal detection model by combining an incremental learning algorithm, detecting the comprehensive health state distribution according to the updated abnormal detection model, and determining drift trend and the key turning points of the current health state distribution by combining a drift detection mechanism;
the method comprises the steps of obtaining fault risks, task importance, maintenance cost and residual service life corresponding to industrial robots, recording the fault risks, task importance, maintenance cost and residual service life as health state influence information, taking the health state influence information as a state space, taking maintenance operation as an action space, defining state transition probability and rewarding functions, updating function values between the state and the action through a reinforcement learning algorithm, generating an optimal maintenance strategy, determining a maintenance rewarding mechanism and a cooperation strategy between each industrial robot by combining a game theory method, repeatedly optimizing, obtaining a global optimal maintenance decision, carrying out cooperative maintenance on the industrial robots, and uploading the state parameters of the industrial robots after maintenance to a server and storing.
The joint probability distribution model is used for describing the correlation and probability distribution among a plurality of random variables, the health state likelihood function represents the conditional probability distribution of observed data under a given health state, the unsupervised learning is a machine learning method for finding hidden modes and structures from unlabeled data, the edge probability refers to the probability distribution of a certain variable in the joint probability distribution and does not consider the values of other variables, the incremental learning is a continuous learning machine learning model, the model can be continuously updated when new data arrive without retraining the whole model, the decision boundary refers to a dividing line or a hyperplane for dividing different categories or states in a feature space, and the drift detection refers to a mechanism for identifying the change of the data distribution along time and is used for timely finding the change of a data generating process.
Dividing an industrial robot system into a plurality of subsystems, such as a mechanical arm, a sensor, a controller and the like, carrying out probability modeling on the health state of each subsystem, regarding the health state as an independent random variable, introducing priori knowledge, such as historical maintenance data, expert experience and the like, constructing prior probability distribution of the health state of each subsystem, calculating posterior probability distribution of the health state of each subsystem based on Bayesian theorem by combining the prior probability and the health state likelihood function of each subsystem, and combining the posterior probability distribution of all the subsystems to obtain comprehensive health state distribution of the whole industrial robot system;
based on the comprehensive health state distribution obtained in advance, an unsupervised learning method, such as a Gaussian mixture model, a hidden Markov model and the like, is adopted to model the comprehensive health state distribution, a sample with the highest value for judging an abnormal detection model, namely a sample with the largest information amount is selected according to an active exploration strategy, such as uncertainty sampling, information gain and the like, the uncertainty of the currently selected sample is evaluated through calculating the edge probability, the sample with the highest uncertainty is selected, the selected sample is marked manually, whether the selected sample is in an abnormal state or not is determined, the marked sample is added into a training set, and the abnormal detection model is updated;
Adopting an increment learning algorithm, such as online learning, progressive learning and the like, adjusting decision boundaries and model super parameters of an abnormal detection model according to a newly marked sample, detecting comprehensive health state distribution by using an updated abnormal detection model, identifying potential abnormal states, introducing a drift detection mechanism, such as concept drift detection, covariance drift detection and the like, monitoring the change trend of the comprehensive health state distribution, and determining the drift trend and key turning points of the health state distribution by combining the drift detection result;
acquiring health state influence information such as fault risk, task importance, maintenance cost and residual life of an industrial robot, taking optional maintenance operations such as preventive maintenance, corrective maintenance and the like as a state space of reinforcement learning, defining state transition probability and rewarding functions as an action space of reinforcement learning, describing the influence of different maintenance operations on the health state and the cost, selecting a reinforcement learning algorithm, generating an optimal maintenance strategy by continuously interacting with the environment, updating a value function between the state and the action, and determining the optimal maintenance operation to be adopted under different health states;
For a scene of cooperative work of a plurality of industrial robots, a game theory method is introduced, such as Nash equilibrium, cooperation and competition relation among modeling robots are established, an allocation scheme and a cooperation strategy of maintenance resources are determined according to health states and maintenance requirements of the robots, a maintenance rewarding mechanism and a cooperation strategy of a multi-robot system are calculated through a game theory algorithm, a maintenance decision of the multi-robot is optimized in an iterative mode, global benefits and local benefits are comprehensively considered, a globally optimal maintenance strategy is obtained, cooperative maintenance is carried out on the industrial robots according to the generated optimal maintenance strategy, such as preventive replacement of parts, sensor calibration and the like, state parameters of the robots, such as running time, load, vibration and the like, are collected in a maintenance process, and the state parameters of the robots after maintenance are uploaded to a central server, and health state information of the robots is updated and stored.
In the embodiment, the overall health condition of the robot can be more accurately depicted by the overall modeling method, a reliable basis is provided for subsequent abnormal detection and maintenance decision making, the most valuable samples are intelligently selected for marking, learning efficiency and accuracy of an abnormal detection model are remarkably improved, the accurate assessment of the health condition of the robot, early warning of abnormality, dynamic optimization of maintenance strategies and collaborative management of multiple robots are achieved by continuously integrating the newly marked samples, the decision boundary and model parameters are adjusted, the high accuracy and timeliness are always kept, multiple factors such as fault risk, task importance, maintenance cost and residual life are comprehensively considered, a reinforcement learning algorithm is adopted for solving, a globally optimal maintenance decision can be obtained, maintenance cost can be remarkably reduced, the availability and production efficiency of the robot are improved, during collaborative maintenance of multiple robots, the game theory method is introduced, maintenance resource allocation and task collaboration among different robots can be effectively coordinated, and maximization of overall benefits is achieved.
In an alternative embodiment of the present invention,
Based on the Bayes theorem, the posterior probability distribution corresponding to each subsystem is calculated by combining the prior probability and the health state likelihood function of each subsystem, and the posterior probability distribution is shown as the following formula:
;
Wherein S g represents the health state of the g-th subsystem, O represents the comprehensive health state evidence observed, P (o|s g) represents the likelihood that the comprehensive health state evidence O is observed under the condition that the g-th subsystem is in the health state S g, P (S g |o) represents the posterior probability that the g-th subsystem is in the health state S g under the condition that the comprehensive health state evidence O, P (S g) represents the prior probability that the g-th subsystem is in the health state S g, P (o|s j) represents the likelihood that the comprehensive health state evidence O is observed under the condition that the j-th subsystem is in the health state S j, P (S j) represents the prior probability that the j-th subsystem is in the health state S j, and n represents the total number of subsystem states.
In the embodiment, the prior probability and the likelihood function can be combined through the bayesian theorem, so that the posterior probability distribution considers the prior knowledge and fully utilizes the observation data, thereby obtaining more reliable and comprehensive health state estimation, quantifying the uncertainty of the health state of each subsystem through the posterior probability distribution, providing more abundant information, supporting more flexible and fine decision, enabling the health state estimation to be adaptively learned and improved in a recursive updating mode, and in conclusion, the embodiment fully utilizes the prior knowledge and the observation data, quantifying the uncertainty of the health state, supporting probabilistic decision making and providing a solid foundation for intelligent maintenance of the industrial robot.
Fig. 2 is a schematic structural diagram of an industrial robot predictive maintenance management system according to an embodiment of the present invention, as shown in fig. 2, the system includes:
The system comprises a first unit, a second unit and a third unit, wherein the first unit is used for acquiring multi-modal heterogeneous data of the industrial robot in real time through a preset sensing network, performing intelligent preprocessing on the multi-modal heterogeneous data, combining blockchain right-determining synchronization and generating an countermeasure network enhancement equalization operation to obtain first preprocessed data, performing cross-modal correlation fusion on the first preprocessed data through a multi-modal graph convolutional neural network to generate fusion data, extracting complementary redundant information among the fusion data and constructing a semantic feature map corresponding to the industrial robot;
The second unit is used for constructing a health state prediction model corresponding to the industrial robot comprising a plurality of subsystems based on the converter model, adding the semantic feature map into the health state prediction model as input, mapping the semantic feature map into an embedded space by an input embedding layer through linear transformation, extracting time sequence information, combining a self-attention layer to perform feature interaction to obtain interaction features, and performing transformation and feature enhancement on the interaction features through a feedforward neural network layer to obtain health state probability distribution of each subsystem;
The third unit is used for generating comprehensive health state distribution through Bayesian inference fusion based on the health state probability distribution, identifying an abnormal mode and key turning points in the comprehensive health state distribution by combining an abnormal detection model based on active learning and combining active exploration and uncertainty sampling, generating an optimal maintenance strategy by combining a reinforcement learning algorithm based on health state influence information, carrying out cooperative maintenance on the industrial robot through a game theory method, uploading a maintenance result to a server in real time, and storing the current state of the industrial robot.
In a third aspect of an embodiment of the present invention,
There is provided an electronic device including:
A processor;
A memory for storing processor-executable instructions;
wherein the processor is configured to invoke the instructions stored in the memory to perform the method described previously.
In a fourth aspect of an embodiment of the present invention,
There is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method as described above.
The present invention may be a method, apparatus, system, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for performing various aspects of the present invention.
It should be noted that the above embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the technical solution described in the above embodiments may be modified or some or all of the technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the scope of the technical solution of the embodiments of the present invention.

Claims (10)

1.工业机器人预测性维护管理方法,其特征在于,包括:1. A predictive maintenance management method for an industrial robot, comprising: 通过预先设置的传感网络实时获取工业机器人的多模态异构数据,对所述多模态异构数据进行智能预处理,结合区块链确权同步以及生成对抗网络增强均衡操作,得到第一预处理数据,通过多模态图卷积神经网络对所述第一预处理数据进行跨模态关联融合,生成融合数据,提取所述融合数据间的互补冗余信息并构建所述工业机器人对应的语义特征图谱;Acquire multimodal heterogeneous data of industrial robots in real time through a pre-set sensor network, perform intelligent preprocessing on the multimodal heterogeneous data, combine blockchain confirmation synchronization and generate adversarial network enhanced balancing operations to obtain first preprocessed data, perform cross-modal correlation fusion on the first preprocessed data through a multimodal graph convolutional neural network to generate fused data, extract complementary redundant information between the fused data and construct a semantic feature map corresponding to the industrial robot; 基于转换器模型构建包含多个子系统的工业机器人对应的健康状态预测模型,将所述语义特征图谱作为输入添加至所述健康状态预测模型中,输入嵌入层通过线性变换将所述语义特征图谱映射至嵌入空间并提取时序信息,结合自注意力层进行特征交互得到交互特征,将所述交互特征通过前馈神经网络层进行变换和特征增强,得到每个子系统的健康状态概率分布;Based on the converter model, a health status prediction model corresponding to an industrial robot including multiple subsystems is constructed, and the semantic feature map is added as input to the health status prediction model. The input embedding layer maps the semantic feature map to the embedding space through linear transformation and extracts the time series information. The interactive features are obtained by combining the self-attention layer for feature interaction. The interactive features are transformed and enhanced through the feedforward neural network layer to obtain the health status probability distribution of each subsystem. 基于所述健康状态概率分布,通过贝叶斯推理融合生成综合健康状态分布,结合基于主动学习的异常检测模型,结合主动探索和不确定性采样识别所述综合健康状态分布中的异常模式和关键转折点,基于健康状态影响信息,结合强化学习算法生成最优维护策略并通过博弈论方法对所述工业机器人进行协同维护,将维护结果实时上传至服务器并保存所述工业机器人的当前状态。Based on the health status probability distribution, a comprehensive health status distribution is generated through Bayesian reasoning fusion, combined with an anomaly detection model based on active learning, combined with active exploration and uncertainty sampling to identify abnormal patterns and key turning points in the comprehensive health status distribution, based on the health status impact information, combined with a reinforcement learning algorithm to generate an optimal maintenance strategy and through game theory methods to perform collaborative maintenance on the industrial robot, the maintenance results are uploaded to the server in real time and the current status of the industrial robot is saved. 2.根据权利要求1所述的方法,其特征在于,通过预先设置的传感网络实时获取工业机器人的多模态异构数据,对所述多模态异构数据进行智能预处理,结合区块链确权同步以及生成对抗网络增强均衡操作,得到第一预处理数据包括:2. The method according to claim 1 is characterized in that the multimodal heterogeneous data of the industrial robot is obtained in real time through a pre-set sensor network, the multimodal heterogeneous data is intelligently preprocessed, and the first preprocessed data is obtained by combining blockchain confirmation synchronization and generating adversarial network enhanced equilibrium operations, including: 在工业机器人的关键部件和关节处预先部署多个类型的传感器组成传感网络,通过所述传感网络实时获取所述工业机器人的多模态异构数据,其中,所述多模态异构数据包括加速度,振动,温度,电流以及位置信息;Pre-deploy multiple types of sensors at key components and joints of the industrial robot to form a sensor network, and obtain multimodal heterogeneous data of the industrial robot in real time through the sensor network, wherein the multimodal heterogeneous data includes acceleration, vibration, temperature, current and position information; 对采集到的多模态异构数据进行智能化预处理,通过小波变换操作对原始数据进行降噪和异常值剔除,结合动态时间规整算法将具有不同采样率和时间戳的异构数据映射到相同的时间尺度上,得到对齐数据,对所述对齐数据进行归一化处理生成标准数据;Intelligent preprocessing is performed on the collected multimodal heterogeneous data. The original data is denoised and outliers are removed through wavelet transform operations. The heterogeneous data with different sampling rates and timestamps are mapped to the same time scale in combination with the dynamic time warping algorithm to obtain aligned data. The aligned data is normalized to generate standard data. 通过深度生成对抗网络对所述标准数据进行数据增强并扩充所述标准数据的规模,结合插值合成算法平衡数据类别分布并通过非对称加密算法进行数据加密,基于区块链的分布式共识机制在多端进行同步和可信共享,得到所述第一预处理数据。The standard data is enhanced and expanded in scale through a deep generative adversarial network, the data category distribution is balanced by combining an interpolation synthesis algorithm and the data is encrypted through an asymmetric encryption algorithm, and the distributed consensus mechanism based on blockchain is used to synchronize and trustfully share data on multiple terminals to obtain the first preprocessed data. 3.根据权利要求1所述的方法,其特征在于,通过多模态图卷积神经网络对所述第一预处理数据进行跨模态关联融合,生成融合数据,提取所述融合数据间的互补冗余信息并构建所述工业机器人对应的语义特征图谱包括:3. The method according to claim 1 is characterized in that the cross-modal association fusion of the first pre-processed data is performed through a multimodal graph convolutional neural network to generate fused data, and the complementary redundant information between the fused data is extracted to construct a semantic feature map corresponding to the industrial robot, comprising: 将所述第一预处理数据映射至预先初始化的异构图结构中,其中,每个传感器节点作为图的节点,节点属性为当前传感器的多维时序特征向量,边为传感器之间的关联强度,边权重为节点间的相关性和依赖关系,初始化节点特征和边权重矩阵;Mapping the first preprocessed data to a pre-initialized heterogeneous graph structure, wherein each sensor node is a node of the graph, the node attribute is the multi-dimensional time series feature vector of the current sensor, the edge is the association strength between sensors, the edge weight is the correlation and dependency between nodes, and the node feature and edge weight matrix are initialized; 在所述异构图结构上应用多模态图卷积神经网络,通过跨模态卷积核和每个模态对应的卷积核函数,分别提取每个模态中的局部特征,结合注意机制为每个模态中的局部特征分配重要性权重,根据所述重要性权重对多个模态的卷积结果进行跨模态关联融合,生成每个节点的联合语义表示,堆叠多层多模态图卷积神经网络层并自适应聚合节点特征,生成跨模态特征;Applying a multimodal graph convolutional neural network on the heterogeneous graph structure, extracting local features in each modality respectively through a cross-modal convolution kernel and a convolution kernel function corresponding to each modality, assigning importance weights to the local features in each modality in combination with an attention mechanism, performing cross-modal association fusion on the convolution results of multiple modalities according to the importance weights, generating a joint semantic representation of each node, stacking multiple layers of multimodal graph convolutional neural network layers and adaptively aggregating node features to generate cross-modal features; 对于所述跨模态特征,基于皮尔逊相关系数计算不同模态之间的相关性矩阵,对所述相关性矩阵进行阈值化处理并生成稀疏相关性矩阵,结合图聚类算法对所述稀疏相关性矩阵进行社区划分,将所述跨模态特征聚集成簇,每个簇内的特征表示一组互补冗余的语义信息,对每个簇内的特征进行重要性分析并结合基于图的特征选择方法识别关键互补冗余特征并将所述关键互补冗余特征添加至预先初始化的冗余集合中;For the cross-modal features, a correlation matrix between different modalities is calculated based on the Pearson correlation coefficient, the correlation matrix is thresholded and a sparse correlation matrix is generated, the sparse correlation matrix is divided into communities in combination with a graph clustering algorithm, the cross-modal features are clustered into clusters, the features in each cluster represent a set of complementary and redundant semantic information, the features in each cluster are analyzed for importance, and a graph-based feature selection method is used to identify key complementary and redundant features, and the key complementary and redundant features are added to a pre-initialized redundant set; 构建语义特征图谱,基于所述冗余集合,使用所述关键互补冗余特征填充语义特征图谱的实例层,明确定义类型层次和属性取值,引入时间维度并建立实例间的语义关联,得到所述语义特征图谱。Construct a semantic feature map, based on the redundant set, use the key complementary redundant features to fill the instance layer of the semantic feature map, clearly define the type hierarchy and attribute values, introduce the time dimension and establish semantic associations between instances, and obtain the semantic feature map. 4.根据权利要求1所述的方法,其特征在于,基于转换器模型构建包含多个子系统的工业机器人对应的健康状态预测模型,将所述语义特征图谱作为输入添加至所述健康状态预测模型中,输入嵌入层通过线性变换将所述语义特征图谱映射至嵌入空间并提取时序信息,结合自注意力层进行特征交互得到交互特征,将所述交互特征通过前馈神经网络层进行变换和特征增强,得到每个子系统的健康状态概率分布包括:4. The method according to claim 1 is characterized in that a health status prediction model corresponding to an industrial robot including multiple subsystems is constructed based on a converter model, the semantic feature map is added as input to the health status prediction model, the input embedding layer maps the semantic feature map to the embedding space through a linear transformation and extracts the time series information, and the interactive features are obtained by combining the self-attention layer for feature interaction, and the interactive features are transformed and enhanced through a feedforward neural network layer to obtain the health status probability distribution of each subsystem, including: 以转换器模型为基础,构建编码器-解码器结构并将工业机器人对应的语义特征图谱添加至所述转换器模型中,通过节点嵌入技术将所述语义特征图谱中的节点映射为低维稠密向量并保留图结构,设计编码器和解码器的结构并引入位置编码机制,在输入嵌入层中添加位置嵌入向量,得到每个子系统对应的健康状态预测模型;Based on the converter model, an encoder-decoder structure is constructed and the semantic feature map corresponding to the industrial robot is added to the converter model. The nodes in the semantic feature map are mapped to low-dimensional dense vectors through node embedding technology and the graph structure is retained. The structure of the encoder and decoder is designed and the position encoding mechanism is introduced. The position embedding vector is added to the input embedding layer to obtain the health status prediction model corresponding to each subsystem. 将所述语义特征图谱作为输入添加至所述健康状态预测模型中并通过输入嵌入层将图谱中的节点通过线性变换映射至低维嵌入空间中,结合图注意力网络聚合邻居节点信息并更新每个节点的嵌入表示,在每个节点嵌入中添加对应的时间戳信息并构建时序嵌入矩阵,基于所述时序嵌入矩阵确定当前节点对应的时序信息,对嵌入时序信息的语义特征图谱进行编码并将图谱划分为多个子图,每个子图对应一个工业机器人的子系统,基于注意力机制对每个子图进行特征提取和特征交互,得到所述交互特征;Add the semantic feature map as input to the health status prediction model and map the nodes in the map to a low-dimensional embedding space through a linear transformation through an input embedding layer, aggregate neighbor node information in combination with a graph attention network and update the embedding representation of each node, add corresponding timestamp information to each node embedding and construct a time series embedding matrix, determine the time series information corresponding to the current node based on the time series embedding matrix, encode the semantic feature map embedded with the time series information and divide the map into multiple subgraphs, each subgraph corresponds to a subsystem of an industrial robot, perform feature extraction and feature interaction on each subgraph based on the attention mechanism, and obtain the interaction feature; 基于所述交互特征,提取不同子系统之间的关联模式,将所述交互特征添加至所述健康状态预测模型中,递归生成下一时间步的健康状态,结合所述关联模式,结合注意力机制动态调整不同子系统对当前预测的影响程度,重复预测直至达到预设的最大迭代次数,得到每个子系统对应的健康状态概率分布。Based on the interaction features, the association patterns between different subsystems are extracted, and the interaction features are added to the health status prediction model. The health status of the next time step is recursively generated. Combined with the association pattern and the attention mechanism, the influence of different subsystems on the current prediction is dynamically adjusted. The prediction is repeated until the preset maximum number of iterations is reached to obtain the health status probability distribution corresponding to each subsystem. 5.根据权利要求4所述的方法,其特征在于,结合图注意力网络聚合邻居节点信息并更新每个节点的嵌入表示如下公式所示:5. The method according to claim 4 is characterized in that the neighbor node information is aggregated in combination with the graph attention network and the embedding representation of each node is updated as shown in the following formula: ; 其中,表示更新后的节点i的隐藏状态,h i 表示节点i的隐藏状态,K表示注意力头的数量,σ表示激活函数,表示第k个注意力头在时间步t下,节点i对邻居节点j的注意力权重,W k 表示第k个注意力头的权重矩阵,h j 表示节点j的隐藏状态,表示第k个注意力头在时间步t下,节点i对自身的注意力权重,N(i)表示节点i的邻居节点集合。in, represents the updated hidden state of node i , hi represents the hidden state of node i , K represents the number of attention heads, σ represents the activation function, represents the attention weight of node i to neighbor node j at time step t , W k represents the weight matrix of the kth attention head, h j represents the hidden state of node j , represents the attention weight of node i on itself at time step t by the kth attention head, and N ( i ) represents the set of neighbor nodes of node i . 6.根据权利要求1所述的方法,其特征在于,基于所述健康状态概率分布,通过贝叶斯推理融合生成综合健康状态分布,结合基于主动学习的异常检测模型,结合主动探索和不确定性采样识别所述综合健康状态分布中的异常模式和关键转折点,基于健康状态影响信息,结合强化学习算法生成最优维护策略并通过博弈论方法对所述工业机器人进行协同维护,将维护结果实时上传至服务器并保存所述工业机器人的当前状态包括:6. The method according to claim 1 is characterized in that, based on the health status probability distribution, a comprehensive health status distribution is generated through Bayesian reasoning fusion, an abnormal detection model based on active learning is combined, and active exploration and uncertainty sampling are combined to identify abnormal patterns and key turning points in the comprehensive health status distribution, based on health status impact information, an optimal maintenance strategy is generated in combination with a reinforcement learning algorithm, and the industrial robot is collaboratively maintained through a game theory method, and the maintenance results are uploaded to a server in real time and the current state of the industrial robot is saved, including: 将每个子系统的健康状态概率分布作为独立的随机变量,建立联合概率分布模型,引入先验知识构建先验分布概率,基于贝叶斯定理,结合先验分布概率和各子系统的健康状态似然函数计算对应每个子系统对应的后验概率分布,并将所述后验概率分布作为所述综合健康状态分布;The health status probability distribution of each subsystem is taken as an independent random variable, a joint probability distribution model is established, prior knowledge is introduced to construct the prior distribution probability, and based on Bayes' theorem, the posterior probability distribution corresponding to each subsystem is calculated by combining the prior distribution probability and the health status likelihood function of each subsystem, and the posterior probability distribution is used as the comprehensive health status distribution; 基于所述综合健康状态分布,构建基于主动学习的异常检测模型并通过无监督学习方法对所述综合健康状态分布进行建模,根据主动探索策略选择具有最大信息量的样本进行标注,通过边缘概率评估当前样本的不确定性,重复评估并选择具有最高不确定性的样本,添加人工标注标签;Based on the comprehensive health status distribution, an anomaly detection model based on active learning is constructed and the comprehensive health status distribution is modeled by an unsupervised learning method, samples with the largest amount of information are selected for annotation according to an active exploration strategy, the uncertainty of the current sample is evaluated by edge probability, the sample with the highest uncertainty is repeatedly evaluated and selected, and a manual annotation label is added; 根据重新标注的样本更新所述异常检测模型,结合增量学习算法自适应调整所述异常检测模型的决策边界和模型超参数,根据更新后异常检测模型对所述综合健康状态分布进行检测,结合漂移检测机制确定当前健康状态分布的漂移趋势和所述关键转折点;The anomaly detection model is updated according to the re-labeled samples, the decision boundary and model hyperparameters of the anomaly detection model are adaptively adjusted in combination with the incremental learning algorithm, the comprehensive health status distribution is detected according to the updated anomaly detection model, and the drift trend of the current health status distribution and the key turning point are determined in combination with the drift detection mechanism; 获取所述工业机器人对应的故障风险、任务重要性、维护成本以及剩余寿命并记为健康状态影响信息,将所述健康状态影响信息作为状态空间,维护操作作为动作空间,定义状态转移概率和奖励函数,通过强化学习算法更新状态和动作之间的函数值,生成最优维护策略,结合博弈论方法确定每个工业机器人之间的维护奖励机制和协作策略,重复优化,得到全局最优维护决策并对所述工业机器人进行协同维护,将维护后的工业机器人状态参数上传至服务器并保存。The failure risk, task importance, maintenance cost and remaining life span corresponding to the industrial robot are obtained and recorded as health status impact information. The health status impact information is used as the state space, and the maintenance operation is used as the action space. The state transition probability and reward function are defined, and the function value between the state and the action is updated through the reinforcement learning algorithm to generate the optimal maintenance strategy. The maintenance reward mechanism and cooperation strategy between each industrial robot are determined in combination with the game theory method. The optimization is repeated to obtain the global optimal maintenance decision and perform collaborative maintenance on the industrial robots. The state parameters of the industrial robots after maintenance are uploaded to the server and saved. 7.根据权利要求6所述的方法,其特征在于,基于贝叶斯定理,结合先验概率和各子系统的健康状态似然函数计算对应每个子系统对应的后验概率分布如下公式所示:7. The method according to claim 6 is characterized in that, based on Bayes' theorem, the posterior probability distribution corresponding to each subsystem is calculated by combining the prior probability and the health status likelihood function of each subsystem as shown in the following formula: ; 其中,S g 表示第g个子系统的健康状态,O表示观察到的综合健康状态证据,P(O|S g )表示在第g个子系统处于健康状态S g 的条件下观察到综合健康状态证据O的似然性,P(S g |O)表示在综合健康状态证据O的条件下第g个子系统处于健康状态S g 的后验概率,P(S g )表示第g个子系统处于健康状态S g 的先验概率,P(O|S j )表示在第j个子系统处于健康状态S j 的条件下观察到综合健康状态证据O的似然性,P(S j )表示第j个子系统处于健康状态S j 的先验概率,n表示子系统状态的总数。Wherein, Sg represents the health state of the g- th subsystem, O represents the observed comprehensive health state evidence, P ( O | Sg ) represents the likelihood of observing the comprehensive health state evidence O under the condition that the g- th subsystem is in the health state Sg , P ( Sg | O ) represents the posterior probability that the g- th subsystem is in the healthy state Sg under the condition that the comprehensive health state evidence O is present, P ( Sg ) represents the prior probability that the g- th subsystem is in the healthy state Sg , P ( O | Sj ) represents the likelihood of observing the comprehensive health state evidence O under the condition that the j- th subsystem is in the healthy state Sj , P ( Sj ) represents the prior probability that the j -th subsystem is in the healthy state Sj , and n represents the total number of subsystem states. 8.工业机器人预测性维护管理系统,用于实现前述权利要求1-7中任一项所述的方法,其特征在于,包括:8. An industrial robot predictive maintenance management system, used to implement the method according to any one of claims 1 to 7, characterized in that it comprises: 第一单元,用于通过预先设置的传感网络实时获取工业机器人的多模态异构数据,对所述多模态异构数据进行智能预处理,结合区块链确权同步以及生成对抗网络增强均衡操作,得到第一预处理数据,通过多模态图卷积神经网络对所述第一预处理数据进行跨模态关联融合,生成融合数据,提取所述融合数据间的互补冗余信息并构建所述工业机器人对应的语义特征图谱;The first unit is used to obtain multimodal heterogeneous data of the industrial robot in real time through a preset sensor network, perform intelligent preprocessing on the multimodal heterogeneous data, combine blockchain confirmation synchronization and generate adversarial network enhanced equilibrium operations to obtain first preprocessed data, perform cross-modal association fusion on the first preprocessed data through a multimodal graph convolutional neural network to generate fused data, extract complementary redundant information between the fused data and construct a semantic feature map corresponding to the industrial robot; 第二单元,用于基于转换器模型构建包含多个子系统的工业机器人对应的健康状态预测模型,将所述语义特征图谱作为输入添加至所述健康状态预测模型中,输入嵌入层通过线性变换将所述语义特征图谱映射至嵌入空间并提取时序信息,结合自注意力层进行特征交互得到交互特征,将所述交互特征通过前馈神经网络层进行变换和特征增强,得到每个子系统的健康状态概率分布;The second unit is used to build a health status prediction model corresponding to an industrial robot including multiple subsystems based on a converter model, add the semantic feature map as input to the health status prediction model, map the semantic feature map to an embedding space through a linear transformation in an input embedding layer and extract temporal information, perform feature interaction in combination with a self-attention layer to obtain interactive features, transform and enhance the interactive features through a feedforward neural network layer, and obtain a health status probability distribution of each subsystem; 第三单元,用于基于所述健康状态概率分布,通过贝叶斯推理融合生成综合健康状态分布,结合基于主动学习的异常检测模型,结合主动探索和不确定性采样识别所述综合健康状态分布中的异常模式和关键转折点,基于健康状态影响信息,结合强化学习算法生成最优维护策略并通过博弈论方法对所述工业机器人进行协同维护,将维护结果实时上传至服务器并保存所述工业机器人的当前状态。The third unit is used to generate a comprehensive health status distribution based on the health status probability distribution through Bayesian reasoning fusion, combine with the anomaly detection model based on active learning, combine active exploration and uncertainty sampling to identify abnormal patterns and key turning points in the comprehensive health status distribution, generate the optimal maintenance strategy based on the health status impact information in combination with the reinforcement learning algorithm, and perform collaborative maintenance on the industrial robot through game theory methods, upload the maintenance results to the server in real time and save the current status of the industrial robot. 9.一种电子设备,其特征在于,包括:9. An electronic device, comprising: 处理器;processor; 用于存储处理器可执行指令的存储器;a memory for storing processor-executable instructions; 其中,所述处理器被配置为调用所述存储器存储的指令,以执行权利要求1至7中任意一项所述的方法。The processor is configured to call the instructions stored in the memory to execute the method described in any one of claims 1 to 7. 10.一种计算机可读存储介质,其上存储有计算机程序指令,其特征在于,所述计算机程序指令被处理器执行时实现权利要求1至7中任意一项所述的方法。10. A computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the method according to any one of claims 1 to 7.
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