CN115270993A - Diesel engine unit state detection method and system - Google Patents
Diesel engine unit state detection method and system Download PDFInfo
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Abstract
The application provides a method and a system for detecting the state of a diesel engine unit, relates to the technical field of computers, and is applied to the diesel engine unit of target equipment, wherein the method comprises the following steps: acquiring initial data of the diesel engine set; preprocessing the initial data to obtain a training sample; training an initial state detection model according to the training sample to obtain a trained state detection model; acquiring a first health degree of each piece of sub-equipment according to the state detection model; and acquiring a second health degree according to the first health degree, wherein the second health degree is used for representing the overall health degree of all the sub-equipment of the diesel engine set. The embodiment of the application can effectively improve the accuracy and efficiency of detection of the diesel engine unit.
Description
Technical Field
The application relates to the technical field of computers, in particular to a method and a system for detecting the state of a diesel engine unit.
Background
In a conventional system for determining the state of a piece of equipment of a ship or a vehicle, an algorithm uses data of all sensor sites at the current moment as input to generate a determination of the overall state of the equipment and the state of each piece of sub-equipment. However, this method is limited as follows:
1. because the system only depends on the current data, the relation of the states of different time points cannot be detected, and the time state mutation of the equipment cannot be identified;
2. if data of a plurality of sensor sites is missing due to faults or communication reasons, the system generates misjudgment.
In order to solve the two problems, a novel main propulsion diesel engine set state judgment algorithm based on time sequence deep learning is provided. In the algorithm, the system combines historical data in a certain time window to reconstruct possible data loss, thereby solving the problem of system misjudgment caused by the data loss of the sensor sites.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a system for detecting the state of a diesel engine unit. The specific technical scheme is as follows:
in a first aspect of an embodiment of the present invention, a method for detecting a state of a diesel engine set is provided, where the method is applied to a diesel engine set of a target device, and includes:
acquiring initial data of the diesel engine set;
preprocessing the initial data to obtain a training sample;
training an initial state detection model according to the training sample to obtain a trained state detection model;
acquiring a first health degree of each piece of sub-equipment according to the state detection model;
and acquiring a second health degree according to the first health degree, wherein the second health degree is used for representing the overall health degree of all the sub-equipment of the diesel engine set.
Optionally, the acquiring initial data of the diesel group includes:
acquiring the number of sub-devices, the number of sensor point positions and the sensor acquisition time of the diesel engine set;
and acquiring first acquisition data of the sensors at all the acquisition moments of the sensors.
Optionally, the preprocessing the initial data to obtain a training sample includes:
performing data cleaning operation on the first acquired data;
and standardizing the first collected data after the data cleaning operation to obtain the training sample.
Optionally, the training of the initial state detection model according to the training samples includes:
preprocessing the training sample, and eliminating illegal values in the training sample to obtain a first training sample;
acquiring a first label of the first training sample, wherein the first label is missing data in the set of the first training sample;
and training the first neural network by using the first training sample, and training by using the first label as a supervision signal to obtain the state detection model containing the trained first neural network.
Optionally, the obtaining the first health degree of each piece of sub-equipment according to the state detection model includes:
acquiring input data corresponding to the initial data;
preprocessing the input data to obtain first input data;
inputting the first input data into the state detection model, and completing the first input data through the first neural network to obtain second input data;
performing convolution processing on the second data input and inputting the second data input to a second neural network of the state detection model to obtain first intermediate data;
and inputting the first intermediate data into a third neural network of the state detection model to obtain the first health degree.
Optionally, the obtaining a second health degree according to the first health degree includes:
processing the first intermediate data to obtain second intermediate data;
and inputting the second intermediate data into the third neural network to obtain the second health degree.
Optionally, the diesel engine set comprises an engine set of target devices, the method further comprising:
performing signal compensation on a crankshaft of the engine block;
a compensated crankshaft tooth period of the crankshaft is calculated.
In another aspect of the embodiments of the present invention, there is provided a diesel group status detection system, including:
the initial data acquisition module is used for acquiring initial data of the diesel engine set;
the training sample acquisition module is used for preprocessing the initial data to obtain a training sample;
the training module is used for training an initial state detection model according to the training sample to obtain a trained state detection model;
the first health degree acquisition module is used for acquiring the first health degree of each piece of sub-equipment according to the state detection model;
and the second health degree acquisition module is used for acquiring a second health degree according to the first health degree, and the second health degree is used for representing the overall health degree of all the sub-equipment of the diesel engine set.
In a further aspect of embodiments of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed, performs the steps of the method as described above.
In a further aspect of the embodiments of the present invention, there is provided a computer device comprising a processor, a memory and a computer program stored on the memory, the processor implementing the steps of the method as described above when executing the computer program.
By last knowing, this application embodiment is when detecting the diesel engine unit state, can be to each sub-equipment sensor data of diesel engine unit, carry out data cleaning and completion to avoid causing the state misjudgment because of the sensor trouble or the data loss of certain position, and can also calculate the mutual influence between each sub-equipment data collection in the testing process, thereby make the prejudgement to the state trend of certain sub-equipment and the whole equipment of diesel engine unit, thereby can greatly promote efficiency and the effect to diesel engine unit state detection.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic view of an application scenario of a diesel engine set state detection system provided in an embodiment of the present application;
FIG. 2 is a schematic flow chart of a diesel engine set state detection method provided by the embodiment of the application;
FIG. 3 is a schematic structural diagram of a diesel engine set state detection system provided by an embodiment of the application;
FIG. 4 is a schematic diagram of an output health of a state detection model provided by an embodiment of the present application;
fig. 5 is an internal structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system," "unit" and/or "module" as used herein is a method for distinguishing between different components, elements, parts, portions or assemblies of different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to or removed from these processes.
FIG. 1 is a schematic diagram of an exemplary diesel engine unit status detection system 100 associated with a vehicle or marine vessel (FIG. 1 illustrates a vehicle diesel engine unit) according to some embodiments of the present application. In some embodiments, the diesel bank status detection system 100 may include a server 110, a network 120, a vehicle 130, and a memory 140.
In some embodiments, the server 110 may be a single server or a group of servers. The set of servers can be centralized or distributed (e.g., the servers 110 can be a distributed system). In some embodiments, the server 110 may be local or remote. For example, the server 110 may access information and/or data stored in the vehicle 130 and/or the memory 140 via the network 120. As another example, server 110 may be directly connected to vehicle 130 and/or memory 140 to access stored information and/or data. In some embodiments, the server 110 may be implemented on a cloud platform or vehicle mount computer. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof. In some embodiments, server 110 may execute on a computing device 200 described in FIG. 2 herein that includes one or more components.
In some embodiments, the server 110 may include a processing engine 112. The processing engine 112 may process information and/or data associated with travel information of the vehicle 130 to perform one or more functions described herein. For example, the processing engine 112 may obtain travel information for the vehicle 130 and determine control parameters that may be used to control the vehicle 130 based on the travel information. In some embodiments, the processing engine 112 may comprise one or more processing engines (e.g., a single chip processing engine or a multi-chip processing engine). By way of example only, processing engine 112 may include a Central Processing Unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction-set processor (ASIP), a Graphics Processing Unit (GPU), a physical computing processor (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (reduced instruction set computer), a microprocessor, or the like, or any combination thereof.
In some embodiments, the server 110 may be connected to the network 120 to communicate with one or more components of the diesel bank status detection system 100 (e.g., the vehicle 130 and the memory 140). In some embodiments, the server 110 may be directly connected to or in communication with one or more components in the diesel bank state detection system 100 (e.g., the vehicle 130 and the memory 140). In some embodiments, the server 110 may be integrated in the vehicle 130.
The vehicle 130 may include the structure of a conventional vehicle such as a chassis, suspension, steering wheel, drive train components, engine, etc. The vehicle 130 may also include at least two sensors (e.g., a distance sensor 131, a speed sensor 132, a position sensor 133, etc.), a brake device 134, an accelerator (not shown), and the like. In some embodiments, the at least two sensors may detect travel information of the vehicle 130. For example, the position sensor 133 may periodically (e.g., every 20 ms) detect the current position of the vehicle 130. For another example, the distance sensor 131 may detect a distance between the current location of the vehicle 130 and a defined location (e.g., the destination 150). As another example, the distance sensor 131 may detect a distance between the current position of the vehicle 130 and other vehicles in the vicinity. As yet another example, the speed sensor 132 may detect the instantaneous speed of the vehicle 130.
In some embodiments, the distance sensor 131 may include a radar, lidar, infrared sensor, or the like, or a combination thereof. The speed sensor 132 may comprise a hall sensor. In some embodiments, the at least two sensors may also include an acceleration sensor (e.g., an accelerometer), a steering angle sensor (e.g., a tilt sensor), a traction-related sensor (e.g., a force sensor), and/or any sensor configured to detect information associated with a dynamic condition of the vehicle 130.
The braking device 134 may be configured for controlling a braking process of the vehicle 130. For example, the braking device 134 may adjust the actual acceleration of the vehicle based on instructions including the target acceleration obtained from the processing engine 112. The accelerator may be configured to control an acceleration process of the vehicle 130.
In some embodiments, the memory 140 may be connected to the network 120 to communicate with one or more components of the diesel bank status detection system 100 (e.g., the server 110 and the vehicle 130). One or more components in the diesel bank status detection system 100 may access data or instructions stored in the memory 140 via the network 120. In some embodiments, the memory 140 may be directly connected to or in communication with one or more components in the diesel bank status detection system 100 (e.g., the server 110 and the vehicle 130). In some embodiments, memory 140 may be part of server 110.
Fig. 2 shows a schematic flow chart of a method and a system for detecting a state of a diesel engine set according to an embodiment of the present application, and as shown in fig. 2, the method and the system for detecting a state of a diesel engine set include the following steps:
and step 210, acquiring initial data of the diesel engine set.
Optionally, step 210 may further include the steps of:
acquiring the number of sub-devices, the number of sensor point positions and the sensor acquisition time of the diesel engine set;
and acquiring first acquisition data of the sensors at all the acquisition moments of the sensors.
In the present application, a diesel engine group refers to a diesel engine sub-device of a target device, for example, the sub-device may be a diesel generator, a diesel engine on a ship or an automobile, or a functional system sub-device such as a diesel water cooling system.
In some embodiments, the symbol appointment operation may be performed first, and specifically, the number n of the sub-devices may be obtained, andmarking the ith sub-device of the n sub-devices as D i ，D i The individual sub-equipment comprises K i A sensor site, wherein the j-th site is denoted as S i,j . Any sensor site S can be further acquired i,j From time t to t + mxΔ t, the first collected data may be specifically expressed as:
the collected data can include various state data of the sub-equipment, such as various collected data related to the sub-equipment, such as power generation, rotating speed, temperature, running time, durability and the like;
the data collected at the position points at the time t are shown, and the time delta t indicates that the data are collected once every time delta t interval by the sensor.
And step 220, preprocessing the initial data to obtain a training sample.
Optionally, step 220 may further include the steps of:
performing data cleaning operation on the first collected data;
and standardizing the first collected data after the data cleaning operation to obtain the training sample.
Specifically, for any
A preprocessing operation may be performed, the preprocessing operation is a normalization process, and the training samples after preprocessing may be specifically expressed as:
wherein when the above-mentioned arbitrary data acquisition
When illegal values such as null, none and the like exist, the original values can be not calculated and kept in the preprocessing process, and t' is a newly introduced time value.
And 230, training an initial state detection model according to the training sample to obtain a trained state detection model.
Optionally, the state detection model may include a first neural network, and step 230 may further include the steps of:
preprocessing the training sample, and removing illegal values in the training sample to obtain a first training sample;
acquiring a first label of the first training sample, wherein the first label is missing data in the set of the first training sample;
and training the first neural network by using the first training sample, and training by using the first label as a supervision signal to obtain the state detection model containing the trained first neural network.
Specifically, the state detection model may be a neural network model for detecting the health of the diesel engine set. The first neural network may be a Hopfield network.
In some embodiments, child device D may be obtained i Training any number k of first neural networks H after constructing a large number of training samples according to m +1 groups of data collected from time t to t + m i,k Specifically, it can be expressed as:
wherein, W theta (H) i,k ) Representing intermediate networks in training, H i,k The input of (a) is the totality of data of the processed sensor sites j over the entire training time window
That is, the first tag may be a true value of the acquired data corresponding to a certain missing moment when the certain missing moment is detected.
In particular toFirst neural network H i,k Illegal values such as null and none values contained in any collected data can be reset to 0, input data are predicted, and the first label is taken as a supervision information number until H i,k Converge to
It can be understood that training first neural network through above mode can be so that first neural network learns how to discern the disappearance data collection at disappearance moment to in the follow-up data collection that can accurately predict the disappearance, with the comprehensiveness and the coherence of guaranteeing follow-up detection subset and whole health degree, and then promote the accuracy that detects.
And 240, acquiring the first health degree of each sub-device according to the state detection model.
Optionally, step 240 may further include the steps of:
acquiring input data corresponding to the initial data;
preprocessing the input data to obtain first input data;
inputting the first input data into the state detection model, and completing the first input data through the first neural network to obtain second input data;
performing convolution processing on the second data input and inputting the second data input to a second neural network of the state detection model to obtain first intermediate data;
and inputting the first intermediate data into a third neural network of the state detection model to obtain the first health degree.
Specifically, after the first neural network is trained, missing data in the collected data may be acquired by using the first neural network, and a data completion operation may be performed to complete a subsequent operation of detecting the health degree.
Specifically, the first input data may be complemented through the first neural network to obtain the second input data, and the second input data may be input to the second neural network for convolutionAnd processing to obtain first intermediate data. Wherein the second neural network may be a ResNet network and the first intermediate data may be represented as
Then will be
Inputting the obtained data into a third neural network MLP network to obtain a first health degree
Wherein,
can be prepared from
Directly obtaining the product after linear regression treatment.
And 250, acquiring a second health degree according to the first health degree, wherein the second health degree is used for representing the overall health degree of all the sub-equipment of the diesel engine set.
Optionally, step 250 may include the steps of:
processing the first intermediate data to obtain second intermediate data;
and inputting the second intermediate data into the third neural network to obtain the second health degree.
In particular, can be to
Integrating to obtain second intermediate data RS t+m Specifically, it can be expressed as:
that is, the first intermediate data at each time may be processed by the Catboost algorithm to obtain the second intermediate data RS t+m 。
Further, the second health degree O can be obtained by combining MLP network and linear regression algorithm t+m 。
Therefore, when the state of the diesel engine set is detected, data cleaning and completion can be carried out on the sensor data of each sub-device of the diesel engine set, so that state misjudgment caused by sensor faults or data loss of a certain point position is avoided, mutual influence among data collected by each sub-device can be calculated in the detection process, prejudgment can be carried out on the state trend of a certain sub-device and the whole device of the diesel engine set, and the efficiency and the effect of detecting the state of the diesel engine set can be greatly improved.
Optionally, the diesel engine set includes an engine set of the target device, and the embodiment of the present application may further include the following steps:
performing signal compensation on a crankshaft of the engine block;
a compensated crankshaft tooth period of the crankshaft is calculated.
Specifically, the compensated crankshaft tooth period is calculated according to the following equation:
T(n)Tcor(n)＝Φinc-(Φcor(q)+Φcor(q+1))Φinc*a
Φ＝Tofs*Φinc*(b+c)
wherein T (q) represents the compensated crankshaft tooth period, phi represents the deviation angle of the crankshaft tooth, phi cor (q + 1) and phi cor (q) are the deviation angles of the q-th tooth and the q + 1-th tooth respectively, phi inc is a single crankshaft tooth angle corresponding to the standard tooth period, tofs represents the crankshaft tooth period deviation ratio, tcor (q) represents the standard tooth period, and a, b and c are preset hyper-parameters or balance parameters.
It can be understood that the method of the embodiment of the application can detect the states of the sub-devices, for example, the sub-devices such as the generator, the engine and the functional systems of the diesel engine set, so as to improve the accuracy of obtaining the second health degree of the whole device.
In order to implement the foregoing method class embodiments, an embodiment of the present application further provides a system for detecting a state of a diesel engine set, and fig. 3 shows a schematic structural diagram of the system for detecting a state of a diesel engine set provided in the embodiment of the present application, where the system includes:
an initial data acquisition module 301, configured to acquire initial data of the diesel engine set;
a training sample obtaining module 302, configured to preprocess the initial data to obtain a training sample;
the training module 303 is configured to train an initial state detection model according to the training sample to obtain a trained state detection model;
a first health degree obtaining module 304, configured to obtain a first health degree of each piece of sub-equipment according to the state detection model;
a second health degree obtaining module 305, configured to obtain a second health degree according to the first health degree, where the second health degree is used to represent an overall health degree of all the sub-devices of the diesel engine set.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working process of the modules/units/sub-units/components in the system described above may refer to the corresponding process in the foregoing method embodiments, and details are not described herein again.
By last knowing, this application embodiment is when detecting the diesel engine unit state, can be to each sub-equipment sensor data of diesel engine unit, carry out data cleaning and completion to avoid causing the state misjudgment because of the sensor trouble or the data loss of certain position, and can also calculate the mutual influence between each sub-equipment data collection in the testing process, thereby make the prejudgement to the state trend of certain sub-equipment and the whole equipment of diesel engine unit, thereby can greatly promote efficiency and the effect to diesel engine unit state detection.
In some embodiments, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The database of the computer device is used for storing relevant data of the image acquisition device. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a diesel unit state detection method and system.
In some embodiments, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 4. The computer device includes a processor, a memory, a communication interface, a display screen, and an input system connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to realize the diesel engine set state detection method and system. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input system of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on a shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the configuration shown in fig. 4 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In some embodiments, there is further provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above method embodiments when executing the computer program.
In some embodiments, a computer-readable storage medium is provided, in which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), for example.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
To sum up, the diesel engine unit state detection method provided by the application comprises the following steps:
acquiring initial data of the diesel engine set;
preprocessing the initial data to obtain a training sample;
training an initial state detection model according to the training sample to obtain a trained state detection model;
acquiring a first health degree of each piece of sub-equipment according to the state detection model;
and acquiring a second health degree according to the first health degree, wherein the second health degree is used for representing the overall health degree of all the sub-equipment of the diesel engine set.
In the embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. The above-described system embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be through some communication interfaces, indirect coupling or communication connection of systems or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments provided in the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus once an item is defined in one figure, it need not be further defined and explained in subsequent figures, and moreover, the terms "first", "second", "third", etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present application. Are intended to be covered by the scope of this application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A diesel unit state detection method is characterized by being applied to a diesel unit of target equipment, and comprises the following steps:
acquiring initial data of the diesel engine set;
preprocessing the initial data to obtain a training sample;
training an initial state detection model according to the training sample to obtain a trained state detection model;
acquiring a first health degree of each piece of sub-equipment according to the state detection model;
and acquiring a second health degree according to the first health degree, wherein the second health degree is used for representing the overall health degree of all the sub-equipment of the diesel engine set.
2. The method for detecting the condition of the diesel engine set according to claim 1, wherein the step of obtaining initial data of the diesel engine set comprises:
acquiring the number of sub-devices, the number of sensor point positions and the sensor acquisition time of the diesel engine set;
and acquiring first acquisition data of the sensors at all the acquisition moments of the sensors.
3. The method for detecting the state of the diesel engine set according to claim 2, wherein the step of preprocessing the initial data to obtain a training sample comprises the steps of:
performing data cleaning operation on the first acquired data;
and standardizing the first acquired data after the data cleaning operation to obtain the training sample.
4. The method according to claim 3, wherein the state detection model comprises a first neural network, and the training of the initial state detection model according to the training samples comprises:
preprocessing the training sample, and eliminating illegal values in the training sample to obtain a first training sample;
acquiring a first label of the first training sample, wherein the first label is missing data in the set of the first training sample;
and training the first neural network by using the first training sample, and training by using the first label as a supervision signal to obtain the state detection model containing the trained first neural network.
5. The method for detecting the state of the diesel engine set according to claim 4, wherein the obtaining the first health degree of each of the sub-devices according to the state detection model comprises:
acquiring input data corresponding to the initial data;
preprocessing the input data to obtain first input data;
inputting the first input data into the state detection model, and completing the first input data through the first neural network to obtain second input data;
performing convolution processing on the second data input and inputting the second data input to a second neural network of the state detection model to obtain first intermediate data;
and inputting the first intermediate data into a third neural network of the state detection model to obtain the first health degree.
6. The method for detecting the state of the diesel engine set according to claim 5, wherein the obtaining a second degree of health according to the first degree of health comprises:
processing the first intermediate data to obtain second intermediate data;
and inputting the second intermediate data into the third neural network to obtain the second health degree.
7. The method according to any one of claims 1 to 6, wherein the diesel group includes an engine group of a target device, the method further comprising:
performing signal compensation on a crankshaft of the engine unit;
a compensated crankshaft tooth period of the crankshaft is calculated.
8. A diesel engine unit condition detection system, the system comprising:
the initial data acquisition module is used for acquiring initial data of the diesel engine set;
the training sample acquisition module is used for preprocessing the initial data to obtain a training sample;
the training module is used for training an initial state detection model according to the training sample to obtain a trained state detection model;
the first health degree acquisition module is used for acquiring the first health degree of each piece of sub-equipment according to the state detection model;
and the second health degree obtaining module is used for obtaining a second health degree according to the first health degree, and the second health degree is used for representing the overall health degree of all the sub-equipment of the diesel engine set.
9. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed, implements the steps of the method according to any one of claims 1 to 7.
10. A computer device comprising a processor, a memory and a computer program stored on the memory, characterized in that the steps of the method according to any of claims 1-7 are implemented when the computer program is executed by the processor.
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