CN118311934B - Electric automatization control system - Google Patents
Electric automatization control system Download PDFInfo
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- CN118311934B CN118311934B CN202410744499.0A CN202410744499A CN118311934B CN 118311934 B CN118311934 B CN 118311934B CN 202410744499 A CN202410744499 A CN 202410744499A CN 118311934 B CN118311934 B CN 118311934B
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
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Abstract
The invention discloses an electric automation control system, which relates to the technical field of electric automation, and utilizes various sensors to acquire electric and mechanical parameter data of equipment, adopts a signal processing technology to accurately monitor and diagnose the problems of bearing abrasion, unbalanced vibration and capacitor aging, greatly improves the monitoring precision and accuracy, accurately predicts the possible fault type and time of the equipment by analyzing historical data and real-time monitoring data, provides more intelligent fault diagnosis and preventive maintenance measures, reduces the downtime and maintenance cost caused by faults, and simultaneously utilizes an automatic control and execution module to automatically adjust the control parameters of the equipment according to the data analysis result and execute corresponding control strategies, reduces the requirement of manual intervention, realizes the real-time monitoring and management of the equipment state, and improves the timeliness and accuracy of fault detection.
Description
Technical Field
The invention relates to the technical field of electric automation, in particular to an electric automation control system.
Background
The electric control system is composed of a plurality of electric elements for realizing control on a certain object or a certain objects, thereby ensuring the safe and reliable operation of the controlled equipment, and the main functions of the electric control system are automatic control, protection, monitoring and measurement, and mainly comprise three parts: input parts such as sensors, switches, buttons, etc., logic parts such as relays, contacts, etc., and an actuating part.
For example, patent publication number CN117712890a discloses an electric automation control system, which comprises a basic mechanism, the basic mechanism comprises an electric cabinet body, the bottom of the inner cavity of the electric cabinet body is fixedly connected with an isolation bearing plate, the electric mechanism is installed in the electric cabinet body, both sides of the top of the isolation bearing plate are provided with limit sliding grooves, the inner side of the limit sliding grooves is slidably provided with a U-shaped sliding plate, and the electric mechanism comprises a movable plate. This electric automation control system, through offer the maintenance mouth and install the heat dissipation back plate in the inside of regulator cubicle body, the back plate that dispels the heat utilizes second gear and cable and electrical control equipment to be connected simultaneously, and collocation threading section of thick bamboo uses, and the setting of these structures can be when maintaining through the process that electrical control equipment pulled out, drives the heat dissipation back plate and rises the maintenance mouth and open, makes the threading section of thick bamboo promote the cable body simultaneously and lifts up, makes things convenient for the staff to maintain and the circuit is dismantled and is detected.
While the above solution has the advantages described above, the conventional electrical automation control system can only monitor the basic state of the equipment, and has limited monitoring capability for bearing wear, unbalanced vibration and capacitor aging potential problems, and manual intervention and judgment are usually required, and under the condition of high dependency, failure diagnosis and maintenance may be caused to be low, and meanwhile, the risk of human misjudgment and incorrect operation is increased, so that the downtime and maintenance cost of the equipment are increased, and therefore, an electrical automation control system is needed to solve such problems.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an electric automation control system, which solves the problems of limited monitoring capability for potential problems of bearing abrasion, unbalanced vibration and capacitor aging in the prior art, generally needs manual intervention and judgment, increases the risk of manual misjudgment and incorrect operation, and increases the downtime and maintenance cost of equipment.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
The invention provides an electrical automation control system, comprising:
The data acquisition module is responsible for acquiring electrical and mechanical parameter data from sensors, wherein the sensors comprise a vibration sensor, a temperature sensor, a capacitance sensor, a current sensor and a pressure sensor;
The data acquisition module comprises:
an interface unit in communication with the sensor, receiving sensor data;
the data processing unit is used for preprocessing the received data;
the running state monitoring module is used for analyzing historical data by utilizing a machine learning algorithm and predicting the running state of the equipment;
The running state monitoring module comprises:
an abnormality detection unit that recognizes abnormal conditions of bearing wear, unbalanced vibration, and capacitor aging;
The prediction model unit predicts the subsequent running state and possible faults of the equipment based on the constructed prediction model and outputs a control strategy;
The execution module is used for automatically adjusting the equipment control parameters according to the data analysis result and executing the corresponding control strategy;
The execution module comprises:
the control unit adjusts control parameters of the equipment according to the prediction result and automatically controls the equipment;
The execution unit executes a control strategy, controls the running state of equipment, and adjusts the rotating speed of the motor and turns on/off the circuit;
the remote management module is used for remotely monitoring, managing and analyzing data of the system;
the remote management module comprises:
a remote communication unit which communicates with the central control center, transmits data and receives instructions;
And the data analysis unit is used for analyzing and processing the sensor data in real time and generating reports and early warning information.
In the abnormality detection unit, the bearing wear recognition method is as follows:
Acquiring vibration signals of a device from a vibration sensor Sampling frequency is，Representing the acquisition time;
Will vibrate the signal Decomposing into local modes on a time-frequency domain, and decomposing the HEMD algorithm by adopting a high-order empirical mode to obtain a local mode function；
For each local pattern functionCalculating energy distribution in frequency domain, and obtaining time-frequency energy distribution matrix by using WIGNERVILLE distribution；
Time-frequency energy distribution matrixDetecting spectral peak value, extracting each local mode functionIs of the dominant frequency of (2)；
By applying individual local pattern functionsCorresponding dominant frequencyCombining and fusing the two to obtain a whole spectrum characteristic to obtain a spectrum characteristic vector，；
For spectrum characteristic vectorExtracting features to obtain feature vectors;
Based on the existing bearing abrasion sample data, the support vector machine SVM is used for carrying out characteristic vectorTraining and establishing an anomaly detection model;
For newly acquired feature vectors Predicting and judging whether bearing wear abnormality exists or not;
The unbalanced vibration anomaly analysis method comprises the following steps:
vibration signal based on vibration sensor acquisition device Performing time-frequency analysis on the vibration signal by adopting a short-time Fourier transform STFT method to obtain a time-frequency diagram，WhereinRepresenting the vibration signalPerforming short-time Fourier transform and time-frequency diagramRepresenting a representation of the vibration signal in the time and frequency domains;
time-frequency diagram Performing energy spectrum calculation to obtain timeFrequency atVibrational energy spectral density at,，Representing the energy distribution of the vibration signal at different frequencies, whereinRepresenting a time-frequency diagramIs indicative of the vibration signal at timeFrequency ofEnergy at;
And then according to the rotation frequency of the equipment Determining an unbalanced vibration frequency range；
Calculating energy concentration in unbalanced vibration frequency range，WhereinAndRepresenting the lower and upper limits of the unbalanced vibration frequency range, respectively;
Setting an energy concentration threshold according to the characteristics of the equipment and the working environment ；
Judging the timeConcentration of energy atWhether or not the set threshold is exceededIf the threshold value is exceeded, determining that an unbalanced vibration abnormality exists;
The capacitor aging abnormality identification mode is as follows:
acquiring voltage signals from capacitor voltage sensors Acquiring a current signal from a capacitor current sensor;
Based on the characteristics of the capacitor and the working environment, a dynamic capacitance model is established, and an extended Kalman filter is used for dynamically estimating the capacitance value, wherein the model is as follows: Wherein, the method comprises the steps of, wherein, Indicating time of dayIs used for the estimation of the capacitance value of (c),The sampling interval is represented by the number of samples,Indicating time of dayIs used for the current signal of the (a),Indicating time of dayA rate of change of the voltage signal of (a);
calculating the rate of change of capacitance of a capacitor ，；
Setting a capacitance change rate threshold according to the aging characteristic and the working environment of the capacitor；
Judging the capacitance change rate of the capacitorWhether or not the set threshold is exceededIf the threshold value is exceeded, determining that the capacitor aging abnormality exists;
the invention is further arranged to: the prediction model construction step comprises the following steps:
Taking bearing abrasion, unbalanced vibration and capacitor aging abnormal conditions identified by the abnormal detection unit as labels, and taking monitored sensor data as characteristics;
Extracting frequency spectrum characteristics and statistical characteristics from sensor data, dividing a processed data set into a training set and a testing set, and marking corresponding equipment states of each sample as normal or abnormal;
Selecting a long-term memory network LSTM, a convolutional neural network CNN and a sequence-to-sequence mode to construct a prediction model;
determining the structures of an input layer, a hidden layer and an output layer of the model according to the data characteristics and the prediction requirements;
Optimizing model parameters of the selected model by adopting random gradient descent SGD by using a training set;
Then evaluating the trained model by using a test set, wherein evaluation indexes comprise accuracy, precision, recall and F1 score, and optimizing the model according to an evaluation result;
Deploying a trained model, and monitoring the state of equipment in real time;
The invention is further arranged to: in the data acquisition module, an interface unit is connected to the sensor, and the state of the sensor is monitored in real time;
analyzing the data sent by the sensors in the interface unit, identifying the type of the data sent by each sensor, and carrying out corresponding processing;
the invention is further arranged to: the data preprocessing unit is used for preprocessing the received original data and comprises filtering, denoising and correcting operations;
the execution module calculates control parameters to be adjusted according to the equipment state and the fault prediction result obtained by the prediction model, and then sends the calculated control parameters to the execution unit;
The execution unit receives the control parameters from the control unit, executes corresponding control actions according to the received control parameters, monitors the running state of the equipment in real time, and feeds the real-time state back to the control unit;
the remote communication unit is used for carrying out data transmission and instruction interaction with the central control center;
the remote communication unit provides a two-way communication function and simultaneously transmits data and receives instructions;
the data analysis unit is used for analyzing and processing the sensor data in real time, generating reports and early warning information and feeding back analysis results to the central control center.
Compared with the prior art, the invention has the following beneficial effects:
According to the invention, the accurate identification of bearing wear, unbalanced vibration and capacitor aging abnormal conditions is realized through a machine learning algorithm, and the comprehensive monitoring and diagnosing capability of the equipment state is improved;
The invention utilizes historical data and a machine learning algorithm to construct a prediction model, predicts the subsequent running state and possible faults of equipment, and outputs a corresponding control strategy; the execution module automatically adjusts equipment control parameters according to the prediction result to realize intelligent control of the running state of the equipment;
According to the invention, the remote management module realizes remote monitoring, management and data analysis of the system, performs data transmission and instruction interaction with the central control center through the remote communication unit, analyzes and processes sensor data in real time, generates report and early warning information, and improves the real-time monitoring and management capability of equipment states;
The invention acquires the electrical and mechanical parameter data of the equipment by utilizing various sensors, accurately monitors and diagnoses the problems of bearing wear, unbalanced vibration and capacitor aging by adopting a signal processing technology, greatly improves the accuracy and precision of monitoring, accurately predicts the possible fault type and time of the equipment by analyzing historical data and real-time monitoring data, provides more intelligent fault diagnosis and preventive maintenance measures, reduces the downtime and maintenance cost caused by the fault, automatically adjusts the control parameters of the equipment according to the data analysis result by utilizing an automatic control and execution module, executes corresponding control strategies, reduces the requirement of manual intervention, realizes the real-time monitoring and management of the equipment state, and improves the timeliness and accuracy of fault detection.
Drawings
Fig. 1 is a diagram of an electrical automation control system framework of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which 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 present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above-described drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention is described in further detail below with reference to the attached drawing figures:
examples
Referring to fig. 1, the present invention provides an electric automation control system, comprising:
The data acquisition module is responsible for acquiring electrical and mechanical parameter data from sensors, wherein the sensors comprise a vibration sensor, a temperature sensor, a capacitance sensor, a current sensor and a pressure sensor;
The data acquisition module comprises:
an interface unit in communication with the sensor, receiving sensor data;
the data processing unit is used for preprocessing the received data;
In the data acquisition module, an interface unit is connected to the sensor, and the state of the sensor is monitored in real time;
analyzing the data sent by the sensors in the interface unit, identifying the type of the data sent by each sensor, and carrying out corresponding processing;
The data preprocessing unit is used for preprocessing the received original data and comprises filtering, denoising and correcting operations;
the running state monitoring module is used for analyzing historical data by utilizing a machine learning algorithm and predicting the running state of the equipment;
The running state monitoring module comprises:
an abnormality detection unit that recognizes abnormal conditions of bearing wear, unbalanced vibration, and capacitor aging;
The prediction model unit predicts the subsequent running state and possible faults of the equipment based on the constructed prediction model and outputs a control strategy;
in the abnormality detection unit, the bearing wear recognition method is as follows:
Acquiring vibration signals of a device from a vibration sensor Sampling frequency is，Representing the acquisition time;
Will vibrate the signal Decomposing into local modes on a time-frequency domain, and decomposing the HEMD algorithm by adopting a high-order empirical mode to obtain a local mode function；
For each local pattern functionCalculating energy distribution in frequency domain, and obtaining time-frequency energy distribution matrix by using WIGNERVILLE distribution；
Time-frequency energy distribution matrixDetecting spectral peak value, extracting each local mode functionIs of the dominant frequency of (2)；
By applying individual local pattern functionsCorresponding dominant frequencyCombining and fusing the two to obtain a whole spectrum characteristic to obtain a spectrum characteristic vector，；
For spectrum characteristic vectorExtracting features to obtain feature vectors;
Based on the existing bearing abrasion sample data, the support vector machine SVM is used for carrying out characteristic vectorTraining and establishing an anomaly detection model;
For newly acquired feature vectors Predicting and judging whether bearing wear abnormality exists or not;
The unbalanced vibration anomaly analysis method comprises the following steps:
vibration signal based on vibration sensor acquisition device Performing time-frequency analysis on the vibration signal by adopting a short-time Fourier transform STFT method to obtain a time-frequency diagram，WhereinRepresenting the vibration signalPerforming short-time Fourier transform and time-frequency diagramRepresenting a representation of the vibration signal in the time and frequency domains;
time-frequency diagram Performing energy spectrum calculation to obtain timeFrequency atVibrational energy spectral density at,，Representing the energy distribution of the vibration signal at different frequencies, whereinRepresenting a time-frequency diagramIs indicative of the vibration signal at timeFrequency ofEnergy at;
And then according to the rotation frequency of the equipment Determining an unbalanced vibration frequency range；
Calculating energy concentration in unbalanced vibration frequency range，WhereinAndRepresenting the lower and upper limits of the unbalanced vibration frequency range, respectively;
Setting an energy concentration threshold according to the characteristics of the equipment and the working environment ；
Judging the timeConcentration of energy atWhether or not the set threshold is exceededIf the threshold value is exceeded, determining that an unbalanced vibration abnormality exists;
The capacitor aging abnormality identification mode is as follows:
acquiring voltage signals from capacitor voltage sensors Acquiring a current signal from a capacitor current sensor;
Based on the characteristics of the capacitor and the working environment, a dynamic capacitance model is established, and an extended Kalman filter is used for dynamically estimating the capacitance value, wherein the model is as follows: Wherein, the method comprises the steps of, wherein, Indicating time of dayIs used for the estimation of the capacitance value of (c),The sampling interval is represented by the number of samples,Indicating time of dayIs used for the current signal of the (a),Indicating time of dayA rate of change of the voltage signal of (a);
calculating the rate of change of capacitance of a capacitor ，；
Setting a capacitance change rate threshold according to the aging characteristic and the working environment of the capacitor；
Judging the capacitance change rate of the capacitorWhether or not the set threshold is exceededIf the threshold value is exceeded, determining that the capacitor aging abnormality exists;
The prediction model construction step comprises the following steps:
Taking bearing abrasion, unbalanced vibration and capacitor aging abnormal conditions identified by the abnormal detection unit as labels, and taking monitored sensor data as characteristics;
Extracting frequency spectrum characteristics and statistical characteristics from sensor data, dividing a processed data set into a training set and a testing set, and marking corresponding equipment states of each sample as normal or abnormal;
Selecting a long-term memory network LSTM, a convolutional neural network CNN and a sequence-to-sequence mode to construct a prediction model;
determining the structures of an input layer, a hidden layer and an output layer of the model according to the data characteristics and the prediction requirements;
Optimizing model parameters of the selected model by adopting random gradient descent SGD by using a training set;
Then evaluating the trained model by using a test set, wherein evaluation indexes comprise accuracy, precision, recall and F1 score, and optimizing the model according to an evaluation result;
Deploying a trained model, and monitoring the state of equipment in real time;
The execution module is used for automatically adjusting the equipment control parameters according to the data analysis result and executing the corresponding control strategy;
The execution module comprises:
the control unit adjusts control parameters of the equipment according to the prediction result and automatically controls the equipment;
The execution unit executes a control strategy, controls the running state of equipment, and adjusts the rotating speed of the motor and turns on/off the circuit;
the execution module calculates control parameters to be adjusted according to the equipment state and the fault prediction result obtained by the prediction model, and then sends the calculated control parameters to the execution unit;
The execution unit receives the control parameters from the control unit, executes corresponding control actions according to the received control parameters, monitors the running state of the equipment in real time, and feeds the real-time state back to the control unit;
the remote management module is used for remotely monitoring, managing and analyzing data of the system;
the remote management module comprises:
a remote communication unit which communicates with the central control center, transmits data and receives instructions;
the data analysis unit is used for analyzing and processing the sensor data in real time and generating reports and early warning information;
the remote communication unit is used for carrying out data transmission and instruction interaction with the central control center;
the remote communication unit provides a two-way communication function and simultaneously transmits data and receives instructions;
the data analysis unit is used for analyzing and processing the sensor data in real time, generating reports and early warning information and feeding back analysis results to the central control center.
According to the invention, the accurate identification of bearing wear, unbalanced vibration and capacitor aging abnormal conditions is realized through a machine learning algorithm, and the comprehensive monitoring and diagnosing capability of the equipment state is improved;
The invention utilizes historical data and a machine learning algorithm to construct a prediction model, predicts the subsequent running state and possible faults of equipment, and outputs a corresponding control strategy; the execution module automatically adjusts equipment control parameters according to the prediction result to realize intelligent control of the running state of the equipment;
According to the invention, the remote management module realizes remote monitoring, management and data analysis of the system, performs data transmission and instruction interaction with the central control center through the remote communication unit, analyzes and processes sensor data in real time, generates report and early warning information, and improves the real-time monitoring and management capability of equipment states;
The invention acquires the electrical and mechanical parameter data of the equipment by utilizing various sensors, accurately monitors and diagnoses the problems of bearing wear, unbalanced vibration and capacitor aging by adopting a signal processing technology, greatly improves the accuracy and precision of monitoring, accurately predicts the possible fault type and time of the equipment by analyzing historical data and real-time monitoring data, provides more intelligent fault diagnosis and preventive maintenance measures, reduces the downtime and maintenance cost caused by the fault, automatically adjusts the control parameters of the equipment according to the data analysis result by utilizing an automatic control and execution module, executes corresponding control strategies, reduces the requirement of manual intervention, realizes the real-time monitoring and management of the equipment state, and improves the timeliness and accuracy of fault detection.
The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (4)
1. An electrical automation control system, comprising:
The data acquisition module is responsible for acquiring electrical and mechanical parameter data from sensors, wherein the sensors comprise a vibration sensor, a temperature sensor, a capacitance sensor, a current sensor and a pressure sensor;
The data acquisition module comprises:
an interface unit in communication with the sensor, receiving sensor data;
the data processing unit is used for preprocessing the received data;
the running state monitoring module is used for analyzing historical data by utilizing a machine learning algorithm and predicting the running state of the equipment;
The running state monitoring module comprises:
an abnormality detection unit that recognizes abnormal conditions of bearing wear, unbalanced vibration, and capacitor aging;
The prediction model unit predicts the subsequent running state and possible faults of the equipment based on the constructed prediction model and outputs a control strategy;
The execution module is used for automatically adjusting the equipment control parameters according to the data analysis result and executing the corresponding control strategy;
The execution module comprises:
the control unit adjusts control parameters of the equipment according to the prediction result and automatically controls the equipment;
The execution unit executes a control strategy, controls the running state of equipment, and adjusts the rotating speed of the motor and turns on/off the circuit;
the remote management module is used for remotely monitoring, managing and analyzing data of the system;
the remote management module comprises:
a remote communication unit which communicates with the central control center, transmits data and receives instructions;
the data analysis unit is used for analyzing and processing the sensor data in real time and generating reports and early warning information;
in the abnormality detection unit, the bearing wear recognition method is as follows:
Acquiring vibration signals of a device from a vibration sensor Sampling frequency is，Representing the acquisition time;
Will vibrate the signal Decomposing into local modes on a time-frequency domain, and decomposing the HEMD algorithm by adopting a high-order empirical mode to obtain a local mode function；
For each local pattern functionCalculating energy distribution in frequency domain, and obtaining time-frequency energy distribution matrix by using WIGNERVILLE distribution；
Time-frequency energy distribution matrixDetecting spectral peak value, extracting each local mode functionIs of the dominant frequency of (2)；
By applying individual local pattern functionsCorresponding dominant frequencyCombining and fusing the two to obtain a whole spectrum characteristic to obtain a spectrum characteristic vector，；
For spectrum characteristic vectorExtracting features to obtain feature vectors;
Based on the existing bearing abrasion sample data, the support vector machine SVM is used for carrying out characteristic vectorTraining and establishing an anomaly detection model;
For newly acquired feature vectors Predicting and judging whether bearing wear abnormality exists or not;
The unbalanced vibration anomaly analysis method comprises the following steps:
vibration signal based on vibration sensor acquisition device Performing time-frequency analysis on the vibration signal by adopting a short-time Fourier transform STFT method to obtain a time-frequency diagram，WhereinRepresenting the vibration signalPerforming short-time Fourier transform and time-frequency diagramRepresenting a representation of the vibration signal in the time and frequency domains;
time-frequency diagram Performing energy spectrum calculation to obtain timeFrequency atVibrational energy spectral density at,，Representing the energy distribution of the vibration signal at different frequencies, whereinRepresenting a time-frequency diagramIs indicative of the vibration signal at timeFrequency ofEnergy at;
And then according to the rotation frequency of the equipment Determining an unbalanced vibration frequency range；
Calculating energy concentration in unbalanced vibration frequency range，WhereinAndRepresenting the lower and upper limits of the unbalanced vibration frequency range, respectively;
Setting an energy concentration threshold according to the characteristics of the equipment and the working environment ；
Judging the timeConcentration of energy atWhether or not the set threshold is exceededIf the threshold value is exceeded, determining that an unbalanced vibration abnormality exists;
The capacitor aging abnormality identification mode is as follows:
acquiring voltage signals from capacitor voltage sensors Acquiring a current signal from a capacitor current sensor;
Based on the characteristics of the capacitor and the working environment, a dynamic capacitance model is established, and an extended Kalman filter is used for dynamically estimating the capacitance value, wherein the model is as follows: Wherein, the method comprises the steps of, wherein, Indicating time of dayIs used for the estimation of the capacitance value of (c),The sampling interval is represented by the number of samples,Indicating time of dayIs used for the current signal of the (a),Indicating time of dayA rate of change of the voltage signal of (a);
calculating the rate of change of capacitance of a capacitor ，；
Setting a capacitance change rate threshold according to the aging characteristic and the working environment of the capacitor；
Judging the capacitance change rate of the capacitorWhether or not the set threshold is exceededIf the threshold is exceeded, it is determined that there is a capacitor aging anomaly.
2. The electrical automation control system of claim 1, wherein the predictive model construction step comprises:
Taking bearing abrasion, unbalanced vibration and capacitor aging abnormal conditions identified by the abnormal detection unit as labels, and taking monitored sensor data as characteristics;
Extracting frequency spectrum characteristics and statistical characteristics from sensor data, dividing a processed data set into a training set and a testing set, and marking corresponding equipment states of each sample as normal or abnormal;
Selecting a long-term memory network LSTM, a convolutional neural network CNN and a sequence-to-sequence mode to construct a prediction model;
determining the structures of an input layer, a hidden layer and an output layer of the model according to the data characteristics and the prediction requirements;
Optimizing model parameters of the selected model by adopting random gradient descent SGD by using a training set;
Then evaluating the trained model by using a test set, wherein evaluation indexes comprise accuracy, precision, recall and F1 score, and optimizing the model according to an evaluation result;
and deploying a trained model, and monitoring the state of the equipment in real time.
3. An electric automation control system according to claim 2, wherein the interface unit is connected to the sensor in the data acquisition module to monitor the state of the sensor in real time;
Analyzing the data sent by the sensors in the interface unit, identifying the type of the data sent by each sensor, and carrying out corresponding processing.
4. An electric automation control system according to claim 3, wherein the data preprocessing unit performs preprocessing on the received raw data, including filtering, denoising and correcting operations;
the execution module calculates control parameters to be adjusted according to the equipment state and the fault prediction result obtained by the prediction model, and then sends the calculated control parameters to the execution unit;
The execution unit receives the control parameters from the control unit, executes corresponding control actions according to the received control parameters, monitors the running state of the equipment in real time, and feeds the real-time state back to the control unit;
the remote communication unit is used for carrying out data transmission and instruction interaction with the central control center;
the remote communication unit provides a two-way communication function and simultaneously transmits data and receives instructions;
the data analysis unit is used for analyzing and processing the sensor data in real time, generating reports and early warning information and feeding back analysis results to the central control center.
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| CN119238903B (en) * | 2024-12-06 | 2025-04-04 | 鸿正联塑(浙江)机械有限公司 | Real-time monitoring method and system for position deviation of turntable of multi-color injection molding machine |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112703457A (en) * | 2018-05-07 | 2021-04-23 | 强力物联网投资组合2016有限公司 | Method and system for data collection, learning and machine signal streaming for analysis and maintenance using industrial internet of things |
| CN118133074A (en) * | 2023-12-28 | 2024-06-04 | 中车永济电机有限公司 | Health diagnosis system PHM suitable for freight locomotive traction system |
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| US10983507B2 (en) * | 2016-05-09 | 2021-04-20 | Strong Force Iot Portfolio 2016, Llc | Method for data collection and frequency analysis with self-organization functionality |
| US20210288493A1 (en) * | 2020-03-12 | 2021-09-16 | ComAp a.s. | Optimization of power generation from power sources using fault prediction based on intelligently tuned machine learning power management |
| KR102264773B1 (en) * | 2020-11-30 | 2021-06-15 | 주식회사 국제기술인증원 | Fault Diagnosis System for Diagnosis and Control of Temperature, Partial Discharge and Noise of Transformers Using Sensors |
| EP4338022A2 (en) * | 2021-05-06 | 2024-03-20 | Strong Force IoT Portfolio 2016, LLC | Quantum, biological, computer vision, and neural network systems for industrial internet of things |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112703457A (en) * | 2018-05-07 | 2021-04-23 | 强力物联网投资组合2016有限公司 | Method and system for data collection, learning and machine signal streaming for analysis and maintenance using industrial internet of things |
| CN118133074A (en) * | 2023-12-28 | 2024-06-04 | 中车永济电机有限公司 | Health diagnosis system PHM suitable for freight locomotive traction system |
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