CN115481692B - SGAN-based water pump unit fault diagnosis method - Google Patents
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Abstract
The invention discloses a water pump unit fault diagnosis method based on SGAN, which comprises the following steps: step 1, acquiring various parameters of equipment when a water pump unit normally operates and various faults occur; step 2, constructing a water pump unit equipment parameter matrixZ M The method comprises the steps of carrying out a first treatment on the surface of the Step 3, constructing a secondary attention generation countermeasure network SGAN, carrying out data synthesis processing on a water pump unit equipment parameter data set, and expanding the data set; and 4, taking the expanded data set as input, calculating a probability value of the fault condition of the expanded data set by adopting a deep belief network DBN, and judging the fault type of the current water pump unit equipment by taking the fault condition with the maximum probability value. The invention adopts a secondary countermeasure network synthesis mechanism, adds an attention mechanism in a secondary network, improves the stability of the quality of synthesized data on the basis of primary synthesized data, and solves the problems of unstable training and uncontrollable data quality of the traditional GAN data.
Description
Technical Field
The invention relates to a water pump unit fault diagnosis method based on SGAN, and belongs to the technical field of industrial equipment fault diagnosis.
Background
Along with the continuous progress of science and technology, the electromechanical equipment in the industrial field has gradually diversified functions and gradually intelligent management, so that the operation working condition of equipment in a complex environment is more changeable, and the maintenance and guarantee cost of the equipment is also higher along with the updating of the equipment. The equipment of the water pump unit is increasingly large and complicated, and the reasons for faults are more and more increased along with the upgrading of the equipment, so that the accurate and effective fault diagnosis of the complex equipment is an effective way for improving the safety of the complex equipment.
In the existing mainstream fault diagnosis technology, deep learning has achieved remarkable results in the field of fault diagnosis by virtue of the strong automatic feature extraction capability. The fault diagnosis method based on deep learning reduces uncertainty of feature extraction and fault diagnosis caused by manual participation in the traditional method, has strong feature extraction capability in the aspect of big data processing, and greatly improves timeliness and practicability of fault diagnosis. However, deep learning has a strong dependence on sufficient fault data, so that accurate diagnosis of small sample fault data cannot be made. In a real scene, certain fault categories of equipment such as a water pump unit and the like only have a small amount of data, so that how to make effective fault diagnosis under the condition of small sample data is very interesting.
In the research of how to solve the small sample data of the fault, the GAN can synthesize new data close to the original data by virtue of the GAN, but the traditional GAN network has the problems of unstable data training, uncontrollable data quality and the like, so that the quality of an expanded data set is influenced, and the subsequent training of a fault classification model is greatly influenced.
Disclosure of Invention
In order to solve the problems, the invention provides a water pump unit fault diagnosis method based on SGAN on the basis of generating a countercheck network, which aims at the problems of data synthesis and expansion of water pump unit fault data under the condition of a small sample, adopts a secondary countercheck network synthesis mechanism, adds an attention mechanism into a secondary network, improves the stability of the quality of synthesized data on the basis of primary synthesized data, and trains a DBN model by using an expanded data set to realize the fault diagnosis of the water pump unit. The method fully utilizes the attention mechanism to integrate the characteristics of the primary countermeasure network synthesized data and the original data, further improves the quality of the synthesized data by utilizing the secondary countermeasure network, and solves the problems of unstable training and uncontrollable data quality of the traditional GAN data.
In order to solve the technical problems, the invention adopts the following technical scheme:
a water pump unit fault diagnosis method based on SGAN comprises the following steps:
step 1, acquiring various parameters of equipment in normal operation of a water pump unit and various parameters of equipment in various faults to obtain a water pump unit equipment parameter data set;
step 2, constructing a water pump unit equipment parameter matrix Z M ；
Step 3, constructing a secondary attention generation countermeasure network SGAN, carrying out data synthesis processing on a water pump unit equipment parameter data set, and expanding the data set;
and 4, taking the expanded data set as input, calculating a probability value of the fault condition of the expanded data set by adopting a deep belief network DBN, and judging the fault type of the current water pump unit equipment by taking the fault condition with the maximum probability value as a basis.
In step 1, various parameters of the equipment during normal operation of the water pump unit and various faults occur, wherein the various parameters of the equipment comprise water pump temperature, water pump pressure, bearing rotating speed and swing degree.
In step 4, the fault conditions include temperature fault, pressure fault, rotation speed fault and swing degree fault.
The step 2 specifically comprises the following steps:
constructing a water pump unit equipment parameter matrix Z M ：
wherein ,em ＝{ID m ,temperature m ,pressure m ,speed m ,swing m ,L}，e m Is the m-th water pump unit equipment parameter vector, ID is the number of the water pump unit equipment, temperature is a temperature parameter, pressure is a pressure parameter, speed is a rotation speed parameter, and swing is a swing parameter;
the water pump unit equipment parameter matrix Z is utilized in the following formula (1) M The out-of-order processing is carried out,
Z noise ＝Z M gRank(m) (1)
obtaining a noise matrix Z noise ，Z noise ＝[e no1 ,e no2 ,e no3 ,K,e nom ] T ；
wherein ,enom Is the mth noise vector.
The step 3 specifically comprises the following steps:
step 3.1, constructing a first-level countermeasure network, and forming a noise matrix Z noise As an input of the primary generator S ', the primary generator S' synthesizes new data from the input data; the primary countermeasure network comprises a primary generator S 'and a primary discriminator P';
step 3.2, the parameter matrix Z of the water pump unit equipment M The new data synthesized by the first-stage generator S 'is used as the input of a first-stage discriminator P', and the first-stage discriminator P 'discriminates whether the new data synthesized by the first-stage generator S' is matched with the water pump unit equipment parameter matrix Z or not M Data in (a) are consistent: if yes, executing the step 3.3; otherwise, the primary countermeasure network feeds back to the primary generator S 'according to the result gradient of the primary discriminator P', and repeats steps 3.1 and 3.2Countermeasure training until the synthesized new data is judged by a first-stage judging device P'; water pump unit equipment parameter matrix Z M The data in (a) is original equipment data; finally output first-level composite information matrix Z N ’：
wherein ,en '＝{ID n ',temperature n ',pressure n ',speed n ',swing n ',L}，e n ' is the device parameter vector of the nth first order synthesis; first-order composite information matrix Z N The data in' is the first-order synthesized device data;
step 3.3, introducing an attention mechanism to realize the interactive fusion of the primary synthesized device data and the original device data, and calculating the pair of the primary synthesized device data and the original device data vector by using the formula (3)<e m ,e n ’>Attention coefficient alpha of (a) m,n ：
α m,n ＝e m ge n ' (3)
Further obtaining the attention vector alpha of the nth first-order synthesized equipment parameter by the formula (4) n ：
And constructing a first-level synthesized device parameter-original device parameter interaction matrix A:
A＝[α 1 ；α 2 ；α 3 ；L；α N ]
wherein N represents N attention vectors;
step 3.4, taking the primary synthesized device parameter-original device parameter interaction matrix A as the input of a secondary generator S' and synthesizing new data; the secondary countermeasure network comprises a secondary generator S "and a secondary arbiter P";
step 3.5, device parameters of the first-order synthesis-originalThe equipment parameter interaction matrix A of the (2) is used as the input of a second-level discriminator P 'for discriminating whether the new data synthesized by the second-level generator S' is matched with the equipment parameter matrix Z of the water pump unit or not as the new data synthesized by the second-level generator S M Data in (a) are consistent: outputting a second-level synthesized data vector if yes; otherwise, the secondary antagonism network feeds back to the secondary generator S ' according to the result gradient of the secondary discriminator P ', and the antagonism training of the step 3.4 and the step 3.5 is repeated until the synthesized new data passes through the discrimination of the secondary discriminator P ';
step 3.6, combining the two-level synthesized device data and the original device data to form an expansion data matrix Z EX ：
wherein ,ef EX ＝{ID f EX ,temperature f EX ,pressure f EX ,speed f EX ,swing f EX ,L}，The device expansion parameter vector of the f second-level synthesis is used for obtaining an expansion data set.
The objective function of the primary countermeasure network is as follows (2):
wherein ,the original equipment parameter vector e is judged to be the expectation of a real sample by a first-level judging device P'; />The device parameter vector representing the first order synthesis is determined by the first order arbiter P' as the expectation of a real sample,wherein S' (e) no ) Representative primary generator S' is based on noise matrix parameter vector e no New data synthesized by the input data; p '(S' (e) no ) Representing the new data as input to the first level arbiter P'.
The objective function of the secondary countermeasure network is as follows (5):
where e represents the original device parameter vector, e' represents the first-order synthesized device parameter vector,the second-level discriminator P' judges the original equipment parameter vector e as the expectation of the real sample; e (E) α～A [log(1-P”(S”(α)))]Representing the secondary arbiter P "in combination with the attention mechanism determines that the secondary synthesized device parameter vector is the expectation of a real sample.
The step 4 specifically comprises the following steps: constructing a DBN model, wherein the DBN model comprises a DBN model bottom formed by sequentially superposing a plurality of limited Boltzmann machines RBM and a model top layer for processing calculation results of the limited Boltzmann machines RBM, and the model top layer is a Softmax classifier;
device extension parameter vector e for two-level synthesis EX Visual layer v of input first layer limited boltzmann machine RBM 1 V, i.e 1 ＝e EX Calculating the visible layer v by equation (6) i Hidden layer l j Probability of activation:
wherein ,wij Representing the visible layer v i Hidden layer l j Is a connection weight of (2); r is (r) i Representing the visible layer v i A bias coefficient;sigmoid (b) is the activation function, b in sigmoid (b) referring to the parenthesis in formula (6);
inputting the final output result of the formula (6) into a Softmax classifier at the top layer of the DBN model, as shown in the formula (7):
and (3) calculating a probability value of a fault condition of the equipment data of the second-level synthesis according to the formula (7), and judging the fault type of the current water pump unit equipment by taking the fault condition with the maximum probability value as a basis.
The limited boltzmann machine RBM is composed of x visible layers v and y hidden layers l;
the hyper-parameter of the limited boltzmann machine RBM is θ= { w, r, u }, where w represents the connection weight of any two visible layers and hidden layers, r represents the visible layer bias coefficient, and u represents the hidden layer bias coefficient.
The invention has the following beneficial effects:
1. aiming at the problem of difficult labeling of fault data of the water pump unit, the invention adopts small sample data as an experimental data set, and can greatly reduce the waste of manpower and time in practical application.
2. The invention provides a water pump unit fault diagnosis method based on SGAN on the basis of generating a contrast network, which aims at the problems of data synthesis and expansion of water pump unit fault data under the condition of a small sample, adopts a secondary contrast network synthesis mechanism, adds an attention mechanism into a secondary network, improves the stability of the quality of synthesized data on the basis of primary synthesized data, and solves the problems of unstable training and uncontrollable data quality of the traditional GAN data.
The invention discloses a fault diagnosis method of a water pump unit based on SGAN (service area network), which aims at the problem that fault samples in monitoring data (including vibration signals, displacement signals, swing signals, temperature signals, rotating speed signals and the like) in the running process of the water pump unit are insufficient, so that equipment faults cannot be accurately and effectively diagnosed in time. According to the invention, the GAN network is introduced to preprocess fault data, so that the problem that the quality of data generated by the traditional GAN model is low, so that a fault diagnosis model cannot be trained more effectively is solved, and the accuracy of a fault diagnosis result can be improved effectively.
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FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic structural view of the present invention.
Detailed Description
The invention will be explained in further detail below with reference to the drawings and embodiments. The specific embodiments described herein are to be considered in an illustrative sense only and are not intended to limit the invention.
Referring to fig. 1-2, the embodiment provides a water pump unit fault diagnosis method based on SGAN, which comprises the following steps,
and step 1, acquiring various parameters of equipment when the water pump unit normally operates (such as water pump temperature, water pump pressure, bearing rotating speed, swing degree and the like) and acquiring a water pump unit equipment parameter data set when various faults occur.
Step 2, constructing a water pump unit equipment parameter matrix Z M ：
wherein ,em ＝{ID m ,temperature m ,pressure m ,speed m ,swing m ,L}，e m Is the m-th device parameter vector, ID is the device number, namely the number of the water pump unit device, temperature is the temperature parameter, pressure is the pressure parameter, speed is the rotation speed parameterSwing is a yaw parameter.
The water pump unit equipment parameter matrix Z is utilized to carry out the operation of (1) M The out-of-order processing is carried out,
Z noise ＝Z M gRank(m) (1)
wherein (1) uses a water pump unit equipment parameter matrix Z M Based on the result obtained in formula (1) and Z M The dimensions of the matrix are the same and are m;
obtaining a noise matrix Z noise ：
Z noise ＝[e no1 ,e no2 ,e no3 ,K,e nom ] T
wherein ,enom Is the m-th noise vector;
step 3, specifically comprising the following steps:
step 3.1, constructing a primary countermeasure network, and setting a water pump unit equipment parameter matrix Z M Disorder processing to obtain a noise matrix Z noise ，Z noise ＝[e no1 ,e no2 ,e no3 ,K,e nom ] T As an input of the primary generator S ', the primary generator S' synthesizes new data from the input data; the primary countermeasure network comprises a primary generator S 'and a primary discriminator P';
step 3.2, the parameter matrix Z of the water pump unit equipment M The new data synthesized by the first-stage generator S 'is used as the input of a first-stage discriminator P', and the first-stage discriminator P 'discriminates whether the new data synthesized by the first-stage generator S' is matched with the original data (namely, the water pump unit equipment parameter matrix Z) M ) And (3) coincidence: if yes, executing the step 3.3; otherwise, the primary countermeasure network feeds back to the primary generator S ' according to the result gradient of the primary discriminator P ', and the countermeasure training of the step 3.1 and the step 3.2 is repeated until the composite data passes the discrimination of the primary discriminator P '.
The objective function of the primary countermeasure network is as follows (2):
wherein ,the original equipment parameter vector e is judged to be the expectation of a real sample by a first-level judging device P'; />The device parameter vector representing the first order synthesis is determined by the first order arbiter P 'as the expectation of the real sample, where S' (e no ) Representative primary generator S' is based on noise matrix parameter vector e no New data synthesized by the input data; p '(S' (e) no ) Representing the new data as input to the first level arbiter P'; finally output first-level composite information matrix Z N ’：
wherein ,en '＝{ID n ',temperature n ',pressure n ',speed n ',swing n ',L}，e n ' is the device parameter vector of the nth first order synthesis;
step 3.3, equipment data for achieving first order Synthesis by attention-directing mechanism (i.e. Z N ' data) and raw water pump equipment data (i.e. Z M Data in (3) and calculating the first-order synthesized device data and the original water pump device data vector pair by using the formula (3)<e m ,e n ’>Attention coefficient alpha of (a) m,n ：
α m,n ＝e m ge n ' (3)
Further obtaining the attention vector alpha of the nth first-order synthesized equipment parameter by the formula (4) n ：
And constructing a first-level synthesized device parameter-original device parameter interaction matrix A:
A＝[α 1 ；α 2 ；α 3 ；L；α N ]
wherein N represents N attention vectors; alpha n An attention vector representing the device parameters of the nth order synthesis;
step 3.4, taking the primary synthesized device parameter-original device parameter interaction matrix A as the input of a secondary generator S' and synthesizing new data; the secondary countermeasure network comprises a secondary generator S "and a secondary arbiter P";
step 3.5, using the first-stage synthesis equipment parameter-original equipment parameter interaction matrix A as the input of a second-stage discriminator P 'for discriminating whether the new data synthesized by the second-stage generator S' is identical to the original data (i.e. the water pump unit equipment parameter matrix Z) M ) And (3) coincidence: outputting a second-level synthesized data vector if yes; otherwise, the secondary antagonism network feeds back to the secondary generator S ' according to the result gradient of the secondary discriminator P ', and the antagonism training of the steps 3.4 and 3.5 is repeated until the synthesized data passes the discrimination of the secondary discriminator P '.
The objective function of the secondary countermeasure network is as follows (5):
where e represents the original water pump's device data vector, i.e., the original device parameter vector, e' represents the first-order synthesized device parameter vector,the second-level discriminator P' judges the original equipment parameter vector e as the expectation of the real sample; e (E) α～A [log(1-P”(S”(α)))]The second-level discriminator P' is combined with an attention mechanism to judge that the second-level synthesized device parameter vector is the expectation of a real sample;
step 3.6, the two-stage synthesized equipment data and the equipment data of the raw water pump unit are combined into an expansion data matrix Z EX ：
wherein ,ef EX ＝{ID f EX ,temperature f EX ,pressure f EX ,speed f EX ,swing f EX ,L}，Is the equipment expansion parameter vector of the f second-level synthesis;
and 4, constructing a DBN model, wherein the bottom of the DBN model is formed by sequentially superposing 5 limited Boltzmann machines (Restricted Boltzmann Machine, RBM), and finally sending the obtained result into a model top-layer Softmax classifier for judging the fault type. The top-level classifier is a softmax classifier that is used to process the results of the 5 RBM calculations. The RBM is composed of x visible layers v and y hidden layers l. The super parameter of the RBM is θ= { w, r, u }, where w represents the connection weight of any two visible layers and the hidden layer, r represents the visible layer bias coefficient, and u represents the hidden layer bias coefficient.
Device extension parameter vector e for two-level synthesis EX Visual layer v for inputting first layer RBM 1 V, i.e 1 ＝e EX Calculating the visible layer v by equation (6) i Hidden layer l j Probability of activation:
wherein ,wij Representing the visible layer v i Hidden layer l j Is a connection weight of (2); r is (r) i Representing the visible layer v i A bias coefficient;sigmoid (b) is the activation function, b in sigmoid (b) referring to the parenthesis in formula (6);
inputting the final output result of the formula (6) into a top classifier of the DBN model, as shown in the formula (7):
and (3) calculating a probability value of a certain fault condition (which can comprise temperature fault, pressure fault, rotating speed fault and swing degree fault, specifically, what needs to be determined according to actual conditions) of the two-stage synthesized equipment data according to the formula (7), and further taking the fault condition with the maximum probability value as a basis to judge the fault state of the current equipment.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules or units or groups of devices in the examples disclosed herein may be arranged in a device as described in this embodiment, or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into a plurality of sub-modules.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or groups of embodiments may be combined into one module or unit or group, and furthermore they may be divided into a plurality of sub-modules or sub-units or groups. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Furthermore, some of the embodiments are described herein as methods or combinations of method elements that may be implemented by a processor of a computer system or by other means of performing the functions. Thus, a processor with the necessary instructions for implementing the described method or method element forms a means for implementing the method or method element. Furthermore, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is for carrying out the functions performed by the elements for carrying out the objects of the invention.
The various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions of the methods and apparatus of the present invention, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention.
In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Wherein the memory is configured to store program code; the processor is configured to perform the method of the invention in accordance with instructions in said program code stored in the memory.
By way of example, and not limitation, computer readable media comprise computer storage media and communication media. Computer-readable media include computer storage media and communication media. Computer storage media stores information such as computer readable instructions, data structures, program modules, or other data. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. Combinations of any of the above are also included within the scope of computer readable media.
As used herein, unless otherwise specified the use of the ordinal terms "first," "second," "third," etc., to describe a general object merely denote different instances of like objects, and are not intended to imply that the objects so described must have a given order, either temporally, spatially, in ranking, or in any other manner.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the above description, will appreciate that other embodiments are contemplated within the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is defined by the appended claims.
Claims (7)
1. The fault diagnosis method of the water pump unit based on the SGAN is characterized by comprising the following steps of:
step 1, acquiring various parameters of equipment in normal operation of a water pump unit and various parameters of equipment in various faults to obtain a water pump unit equipment parameter data set;
step 2, constructing a water pump unit equipment parameter matrix Z M ；
Step 3, constructing a secondary attention generation countermeasure network SGAN, carrying out data synthesis processing on a water pump unit equipment parameter data set, and expanding the data set;
step 4, taking the expanded data set as input, calculating a probability value of a fault condition of the expanded data set by adopting a deep belief network DBN, and judging the fault type of the current water pump unit equipment by taking the fault condition with the maximum probability value as a basis;
the step 2 specifically comprises the following steps:
constructing a water pump unit equipment parameter matrix Z M ：
wherein ,em ＝{ID m ,temperature m ,pressure m ,speed m ,swing m ,L}，e m Is the m-th water pump unit equipment parameter vector, ID is the number of the water pump unit equipment, temperature is a temperature parameter, pressure is a pressure parameter, speed is a rotation speed parameter, and swing is a swing parameter;
the water pump unit equipment parameter matrix Z is utilized in the following formula (1) M The out-of-order processing is carried out,
Z noise ＝Z M gRank(m) (1)
obtaining a noise matrix Z noise ，Z noise ＝[e no1 ,e no2 ,e no3 ,K,e nom ] T ；
wherein ,enom Is the m-th noise vector;
the step 3 specifically comprises the following steps:
step 3.1, constructing a first-level countermeasure network, and forming a noise matrix Z noise As an input of the primary generator S ', the primary generator S' synthesizes new data from the input data; the primary countermeasure network comprises a primary generator S 'and a primary discriminator P';
step 3.2, the parameter matrix Z of the water pump unit equipment M The new data synthesized by the first-stage generator S 'is used as the input of a first-stage discriminator P', and the first-stage discriminator P 'discriminates whether the new data synthesized by the first-stage generator S' is matched with the water pump unit equipment parameter matrix Z or not M Data in (a) are consistent: if yes, executing the step 3.3; otherwise, the primary countermeasure network feeds back to the primary generator S ' according to the result gradient of the primary discriminator P ', and the countermeasure training of the step 3.1 and the step 3.2 is repeated until the synthesized new data passes through the discrimination of the primary discriminator P '; water pump unit equipment parameter matrix Z M The data in (a) is original equipment data; finally output first-level composite information matrix Z N ’：
wherein ,en '＝{ID n ',temperature n ',pressure n ',speed n ',swing n ',L}，e n ' is the device parameter vector of the nth first order synthesis; first-order composite information matrix Z N The data in' is the first-order synthesized device data;
step 3.3, introducing an attention mechanism to realize the interactive fusion of the primary synthesized device data and the original device data,calculating a first-order composite device data and original device data vector pair using equation (3)<e m ,e n ’>Attention coefficient alpha of (a) m,n ：
α m,n ＝e m ge n ' (3)
Further obtaining the attention vector alpha of the nth first-order synthesized equipment parameter by the formula (4) n ：
And constructing a first-level synthesized device parameter-original device parameter interaction matrix A:
A＝[α 1 ；α 2 ；α 3 ；L；α N ]
wherein N represents N attention vectors;
step 3.4, taking the primary synthesized device parameter-original device parameter interaction matrix A as the input of a secondary generator S' and synthesizing new data; the secondary countermeasure network comprises a secondary generator S "and a secondary arbiter P";
step 3.5, taking the first-stage synthesized equipment parameter-original equipment parameter interaction matrix A as new data synthesized by the second-stage generator S 'as input of a second-stage discriminator P', and judging whether the new data synthesized by the second-stage generator S 'is matched with the water pump unit equipment parameter matrix Z by the second-stage discriminator P' M Data in (a) are consistent: outputting a second-level synthesized data vector if yes; otherwise, the secondary antagonism network feeds back to the secondary generator S ' according to the result gradient of the secondary discriminator P ', and the antagonism training of the step 3.4 and the step 3.5 is repeated until the synthesized new data passes through the discrimination of the secondary discriminator P ';
step 3.6, combining the two-level synthesized device data and the original device data to form an expansion data matrix Z EX ：
wherein ,ef EX ＝{ID f EX ,temperature f EX ,pressure f EX ,speed f EX ,swing f EX ,L}，The device expansion parameter vector of the f second-level synthesis is used for obtaining an expansion data set.
2. The method according to claim 1, wherein in step 1, the parameters of the equipment in normal operation of the water pump unit and the parameters of the equipment in various faults include water pump temperature, water pump pressure, bearing rotation speed and swing.
3. The method of claim 1, wherein in step 4, the fault conditions include temperature faults, pressure faults, rotational speed faults, and yaw faults.
4. The method of claim 1, wherein the objective function of the primary countermeasure network is as in equation (2):
wherein ,the original equipment parameter vector e is judged to be the expectation of a real sample by a first-level judging device P'; />The device parameter vector representing the first order synthesis is determined by the first order arbiter P 'as the expectation of the real sample, where S' (e no ) Representative primary generator S' is based on noise matrix parameter vector e no New data synthesized by the input data;P'(S'(e no ) Representing the new data as input to the first level arbiter P'.
5. The method of claim 1, wherein the objective function of the secondary antagonism network is as in equation (5):
where e represents the original device parameter vector, e' represents the first-order synthesized device parameter vector,the second-level discriminator P' judges the original equipment parameter vector e as the expectation of the real sample; e (E) α～A [log(1-P”(S”(α)))]Representing the secondary arbiter P "in combination with the attention mechanism determines that the secondary synthesized device parameter vector is the expectation of a real sample.
6. The method according to claim 1, characterized in that step 4 comprises in particular the steps of: constructing a DBN model, wherein the DBN model comprises a DBN model bottom formed by sequentially superposing a plurality of limited Boltzmann machines RBM and a model top layer for processing calculation results of the limited Boltzmann machines RBM, and the model top layer is a Softmax classifier;
device extension parameter vector e for two-level synthesis EX Visual layer v of input first layer limited boltzmann machine RBM 1 V, i.e 1 ＝e EX Calculating the visible layer v by equation (6) i Hidden layer l j Probability of activation:
wherein ,wij Representing the visible layer v i Hidden layer l j Is a connection weight of (2); r is (r) i Representing the visible layer v i A bias coefficient;sigmoid (b) is an activation function;
inputting the final output result of the formula (6) into a Softmax classifier at the top layer of the DBN model, as shown in the formula (7):
and (3) calculating a probability value of a fault condition of the equipment data of the second-level synthesis according to the formula (7), and judging the fault type of the current water pump unit equipment by taking the fault condition with the maximum probability value as a basis.
7. The method according to claim 6, characterized in that the limited boltzmann machine RBM is composed of x visible layers v and y hidden layers i;
the hyper-parameter of the limited boltzmann machine RBM is θ= { w, r, u }, where w represents the connection weight of any two visible layers and hidden layers, r represents the visible layer bias coefficient, and u represents the hidden layer bias coefficient.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110414601A (en) * | 2019-07-30 | 2019-11-05 | 南京工业大学 | Photovoltaic module method for diagnosing faults, system and equipment based on depth convolution confrontation network |
| WO2021042828A1 (en) * | 2019-09-04 | 2021-03-11 | 华为技术有限公司 | Neural network model compression method and apparatus, and storage medium and chip |
| WO2022048652A1 (en) * | 2020-09-04 | 2022-03-10 | 中兴通讯股份有限公司 | Fault location method, electronic device and storage medium |
| CN114298267A (en) * | 2021-10-13 | 2022-04-08 | 华南理工大学 | Fault diagnosis method based on bidirectional attention generation countermeasure network and application |
| CN114491823A (en) * | 2022-03-28 | 2022-05-13 | 西南交通大学 | Train bearing fault diagnosis method based on improved generation countermeasure network |
| CN114563186A (en) * | 2022-01-21 | 2022-05-31 | 苏州大学 | Rolling bearing unbalance fault diagnosis method and system |
| CN114858455A (en) * | 2022-05-25 | 2022-08-05 | 合肥工业大学 | Rolling bearing fault diagnosis method and system based on improved GAN-OSNet |
| CN114997211A (en) * | 2022-04-22 | 2022-09-02 | 南京航空航天大学 | Cross-working-condition fault diagnosis method based on improved countermeasure network and attention mechanism |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109933881A (en) * | 2019-03-06 | 2019-06-25 | 武汉大学 | A kind of Fault Diagnosis of Power Electronic Circuits method based on optimization deepness belief network |
-
2022
- 2022-09-23 CN CN202211163711.1A patent/CN115481692B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110414601A (en) * | 2019-07-30 | 2019-11-05 | 南京工业大学 | Photovoltaic module method for diagnosing faults, system and equipment based on depth convolution confrontation network |
| WO2021042828A1 (en) * | 2019-09-04 | 2021-03-11 | 华为技术有限公司 | Neural network model compression method and apparatus, and storage medium and chip |
| WO2022048652A1 (en) * | 2020-09-04 | 2022-03-10 | 中兴通讯股份有限公司 | Fault location method, electronic device and storage medium |
| CN114298267A (en) * | 2021-10-13 | 2022-04-08 | 华南理工大学 | Fault diagnosis method based on bidirectional attention generation countermeasure network and application |
| CN114563186A (en) * | 2022-01-21 | 2022-05-31 | 苏州大学 | Rolling bearing unbalance fault diagnosis method and system |
| CN114491823A (en) * | 2022-03-28 | 2022-05-13 | 西南交通大学 | Train bearing fault diagnosis method based on improved generation countermeasure network |
| CN114997211A (en) * | 2022-04-22 | 2022-09-02 | 南京航空航天大学 | Cross-working-condition fault diagnosis method based on improved countermeasure network and attention mechanism |
| CN114858455A (en) * | 2022-05-25 | 2022-08-05 | 合肥工业大学 | Rolling bearing fault diagnosis method and system based on improved GAN-OSNet |
Non-Patent Citations (3)
| Title |
|---|
| Wenlong Fu等.Rolling Bearing Fault Diagnosis in Limited Data Scenarios Using Feature Enhanced Generative Adversarial Networks.《IEEE Sensors Journal 》.2022,第22卷(第9期),8749-8759. * |
| 张苏颖等.基于深度置信网络和对称点模式电机轴承故障诊断研究.《噪声与振动控制》.2022,第42卷(第03期),98-104+143. * |
| 文成林等.基于深度学习的故障诊断方法综述.《电子与信息学报》.2020,(第01期),239-253. * |
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