CN108764341A - A kind of adaptive deep neural network model of operating mode and variable working condition method for diagnosing faults - Google Patents

A kind of adaptive deep neural network model of operating mode and variable working condition method for diagnosing faults Download PDF

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CN108764341A
CN108764341A CN201810530399.2A CN201810530399A CN108764341A CN 108764341 A CN108764341 A CN 108764341A CN 201810530399 A CN201810530399 A CN 201810530399A CN 108764341 A CN108764341 A CN 108764341A
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张博
李伟
江帆
张梦
佟哲
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China University of Mining and Technology CUMT
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Abstract

The edge distribution that the present invention is directed to failure under different working conditions is identical, but it is distributed in the characteristics of changing on scale and position per the condition of class fault sample, a kind of adaptive deep neural network model of operating mode and variable working condition method for diagnosing faults are provided, the adaptive deep neural network model of the operating mode is made of five parts, including source domain characteristic extracting module, fault grader, target domain characteristic extracting module, Location Scale conversion module and field difference regularization module.For the fault sample of source domain after source domain characteristic extracting module and the processing of Location Scale conversion module, the condition distribution that condition is distributed fault sample similar with target domain is similar.The present invention overcomes the differences that sensing data under the conditions of variable working condition is distributed, operating mode influence can be eliminated by providing one kind, and the method for obtaining the information for only reflecting rolling bearing fault or performance degradation has high promotional value to keep the diagnosis of rolling bearing fault more accurate.

Description

A kind of adaptive deep neural network model of operating mode and variable working condition method for diagnosing faults
Technical field
The present invention relates to Fault Diagnosis of Roller Bearings, the adaptive deep neural network model of especially a kind of operating mode and Variable working condition method for diagnosing faults belongs to mechanical fault diagnosis field.
Background technology
Rolling bearing is in electric power, petrochemical industry, metallurgy, machinery, aerospace and some war industry departments using most wide General machine components, and most easy damaged one of component.It is with efficient, frictional resistance is small, easy to assembly, lubrication is easily real The advantages that existing, using very universal on rotating machinery, and plays key effect.Many failures of rotating machinery all with rolling Dynamic bearing has close association.According to relevant statistics, the 70% of mechanical breakdown is vibration fault, and is had in vibration fault 30% is caused by rolling bearing.This is because rolling bearing plays the work for bearing load and transmitting load in mechanical equipment With, and operating condition is more severe, and long continuous operation is easy to be damaged and break down under top load, high rotating speed. Direct result caused by rolling bearing fault gently then reduces and loses certain functions of system, heavy then cause serious even calamity The accident of difficulty.Therefore, the method for diagnosing faults of rolling bearing, be always the technology given priority in mechanical fault diagnosis it One, there is important social and economic significance.
Often changeable (load, rotating speed etc. continuously or intermittently become operating condition rolling bearing in mechanical equipment Change).There are direct correlation relationships with operating mode for collected transducing signal.When system variable parameter operation, new data continues to bring out, former It is first available to there are tag sensor data to produce distributional difference with the test sample under new working condition.Existing training sample It has been not enough to training and has obtained a reliable fault diagnosis model.Meanwhile the failure under the new working condition of a batch is marked again Sample is not only time-consuming and laborious but also very expensive.
This just causes a major issue of rolling bearing fault diagnosis, that is, how using on a small quantity under the conditions of variable working condition Have label training sample or source domain data, establish a reliable model to new working condition or target domain data into Row prediction (source domain data and target domain data can not have identical data distribution).
Invention content
Technical problem to be solved by the present invention lies in the difference that sensing data is distributed under the conditions of variable working condition is overcome, provide One kind can eliminate operating mode influence, and the method for obtaining the information for only reflecting rolling bearing fault or performance degradation, and accurately sentence The diagnostic method of disconnected rolling bearing fault.
In order to solve the above-mentioned technical problem, the present invention devises the rolling bearing fault diagnosis side under the conditions of a kind of variable working condition Method, this method be based on a kind of adaptive deep neural network model of operating mode, the adaptive deep neural network model of the operating mode it is defeated Enter be vibration signal x the ∈ X, feature space X of different working condition lower bearings can be original vibration signal by quick Fu The spectral vectors obtained after leaf transformation export as the operating mode type belonging to fault type label y ∈ Y={ 1,2 ..., K } and sample Label d ∈ { 0,1 }.Assuming thatWithIt indicates the distribution situation of fault sample under different operating modes, is denoted as source domain distribution respectively It is distributed with target domain.WithIndicate the edge distribution situation of vibration signal under different operating modes.If vibration signal xiIt comes from Source domain, i.e.,So di=0.If vibration signal xiFrom target domain, i.e.,So di=1.
Experiment shows under different working conditions that source domain is identical with the edge distribution of target domain failure, but fault sample Condition distribution it is variant, that is,By further studying, it has been found that the original of fault sample Beginning vibration signal is only the change on scale and position after characteristic extracting module, between the condition distribution per class fault sample Change.Therefore, for failure yi∈ Y, it is (W that we, which can find a parameter,i,bi) linear transformation, make the failure sample of source domain This is after the linear change, condition distributionThe condition of fault sample similar with target domain point ClothIt is similar.
Since the sample of target domain lacks faulty tag, we can not directly obtain target domain failure yiThe condition of sample DistributionBut it considersAccording to Bayesian formula, if SoThat is all kinds of fault samples of source domain respectively after linear transformation, New sample has edge distribution identical with target domain sample.Therefore, we can by minimizing following distributional difference, Obtain the corresponding linear change parameter of all kinds of failures
Wherein MMD indicates Maximum Mean Discrepancy, is distributional difference between a kind of common measurement sample Method.
The adaptive deep neural network model of operating mode proposed by the present invention includes 5 parts：
1, source domain characteristic extracting module MS：MSIncluding 5 layer 1 dimension convolutional neural networks layer (Conv1~Conv5) and 2 entirely Articulamentum (FC1, FC2)；The source domain vibration signal of inputFirst pass around Fast Fourier Transform (FFT) (Fast Fourier Transform, abbreviation FFT) processing, then input first layer convolutional neural networks layer (Conv1)；The last one full articulamentum (FC2) include K neuron of quantity identical with fault type；Source domain samplePass through MS5 layer 1 dimension convolutional neural networks Layer is mapped as feature vector with 2 layers of full articulamentum
2, fault grader C：Fault diagnosis essence is a multicategory classification problem, we use Soft-max regression models Estimate the source domain vibration signal of inputBelong to the probability of each fault category, that is,
Wherein,It isJ-th of element value；It, can be with for given source domain sample set Estimate source domain characteristic extracting module M by maximizing following cost functionSParameter,
Wherein,It isJ-th of element value, i.e.,Belong to the probability of each fault category j；
3, target domain characteristic extracting module MT：MTWith MSWith identical network structure；The target domain of input shakes Dynamic signalIt is mapped as feature vector by 5 layer of 1 dimension convolutional neural networks layer and 2 layers of full articulamentum In view of the sample of target domain does not have the label of fault category, so using MSIn 5 layer of 1 dimension convolutional neural networks layer and 2 layers The parameter of full articulamentum initializes MT；
4, Location Scale conversion module LS：K class failures need K Location Scale conversion moduleLSiParameter It is denoted as (Wi,bi), output is expressed asFault sample i.e. in source domain after linear change, wherein yiIndicate failure classes Type；
5, field difference regularization term module：Use the distributional difference of sample between field after MMD expression linear transformations.Pass through The distributional difference is minimized, estimates model parameter, including target domain characteristic extracting module MTIn 5 layer 1 dimension convolutional neural networks The parameter of layer and the parameter and K Location Scale conversion module of 2 layers of full articulamentum
The present invention proposes the Fault Diagnosis of Roller Bearings under the conditions of a kind of variable working condition, including：
Step 1：Training source domain characteristic extracting module MS：Input source domain has exemplar, using back-propagation algorithm, Minimize cost function LclsEstimate source domain characteristic extracting module MSIn 5 layer of 1 dimension convolutional neural networks layer and 2 layers of full articulamentum Parameter：
Wherein,It isJ-th of element value, i.e.,Belong to the general of each fault category j Rate；
Step 2：Use MSIn the parameter of 5 layer of 1 dimension convolutional neural networks layer and 2 layers of full articulamentum initialize MT；
Step 3：Training objective domain features extraction module MT, Location Scale conversion module LS：Input source domain has label Sample and target domain unlabeled exemplars minimize following distributional difference using back-propagation algorithm：
Estimate target domain characteristic extracting module MTIn 5 layer of 1 dimension convolutional neural networks layer and 2 layers of full articulamentum parameter and The parameter of K Location Scale conversion module；
Step 4：Fault diagnosis：Target domain fault sampleBy target domain characteristic extracting module MTIt is defeated after processing Go out forIt is calculated by the following formula sampleFault typeThat is the corresponding failure of most probable value Type：
Wherein,It isJ-th of element value.
Advantageous effect：The edge distribution that the present invention is directed to failure under different working conditions is identical, but per class fault sample Condition is distributed in the characteristics of changing on scale and position, devises the adaptive deep neural network model of operating mode and corresponding Variable working condition method for diagnosing faults overcomes the difference of sensing data distribution under the conditions of variable working condition, and work can be eliminated by providing one kind Condition influences, and the method for obtaining the information for only reflecting rolling bearing fault or performance degradation, to make examining for rolling bearing fault It is disconnected more accurate, there is high promotional value.
Description of the drawings
Fig. 1 is the adaptive deep neural network model structural schematic diagram of operating mode of the present invention；
Fig. 2 is the source domain characteristic extracting module M of the present inventionSStructural schematic diagram；
Fig. 3 is the flow diagram of the variable working condition Fault Diagnosis of Roller Bearings of the present invention.
Specific implementation mode
It elaborates to the present invention below in conjunction with attached drawing.
As shown in Figs. 1-2, the adaptive deep neural network model of operating mode provided by the invention, including, 1 source domain feature Extraction module MS, 1 fault grader C, 1 target domain characteristic extracting module MT, 4 Location Scale conversion module LS：Respectively Corresponding four class failures, i.e. inner ring failure (IF), outer ring failure (OF), rolling element failure (BF) and normal condition (NO)；1 field Difference regularization term module；Dotted portion indicates to need the parameter using back-propagation algorithm estimation response, bold portion in figure Statement network parameter has determined.
As shown in Fig. 2, source domain characteristic extracting module MSAs a part for the adaptive deep neural network model of operating mode, Including 5 convolutional neural networks layers, 2 full articulamentums；The effect of the module is to obtain the nerve for having identification to failure Network parameter；Dotted portion expression needs to estimate corresponding parameter using back-propagation algorithm in figure；Input source domain has label Sample minimizes cost function L using back-propagation algorithmclsEstimate source domain characteristic extracting module MSThe parameter of each layer.
As shown in figure 3, the Fault Diagnosis of Roller Bearings under the conditions of a kind of variable working condition includes the following steps：
Step 1：Training source domain characteristic extracting module MS：Input source domain has exemplar, using back-propagation algorithm, Minimize cost function LclsEstimate source domain characteristic extracting module MSIn 5 layer of 1 dimension convolutional neural networks layer and 2 layers of full articulamentum Parameter：
Wherein,It isJ-th of element value, i.e.,Belong to the general of each fault category j Rate；
Step 2：Use MSIn the parameter of 5 layer of 1 dimension convolutional neural networks layer and 2 layers of full articulamentum initialize MT；
Step 3：Training objective domain features extraction module MT, Location Scale conversion module LS：Input source domain has label Sample and target domain unlabeled exemplars minimize following distributional difference using back-propagation algorithm：
Estimate target domain characteristic extracting module MTIn 5 layer of 1 dimension convolutional neural networks layer and 2 layers of full articulamentum parameter and The parameter of K Location Scale conversion module；
Step 4：Fault diagnosis：Target domain fault sampleBy target domain characteristic extracting module MTIt is defeated after processing Go out forIt is calculated by the following formula sampleFault typeThat is the corresponding failure of most probable value Type：
Wherein,It isJ-th of element value.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art For member, several improvement can also be made under the premise of not departing from inventive principle, including increase fault type, these improvement It should be regarded as protection scope of the present invention.

Claims (3)

1. the Fault Diagnosis of Roller Bearings under the conditions of a kind of variable working condition, it is characterised in that：This method is based on a kind of operating mode certainly Deep neural network model is adapted to, which includes source domain characteristic extracting module MS, fault grader C, target domain feature Extraction module MT, Location Scale conversion module LS and field difference regularization module；The source domain characteristic extracting module MSIncluding 5 layer of 1 dimension convolutional neural networks layer (Conv1~Conv5) and 2 full articulamentums (FC1, FC2), target domain characteristic extracting module MTWith source domain characteristic extracting module MSNetwork structure it is identical；The fault sample of source domain passes through target domain feature extraction mould Block MTAfter the LS processing of Location Scale conversion module, the condition that condition is distributed fault sample similar with target domain is distributed phase Seemingly；Include the following steps：
Step 1：Training source domain characteristic extracting module MS：Input source domain has exemplar, minimum using back-propagation algorithm Change cost function LclsEstimate source domain characteristic extracting module MSThe parameter of each layer：
Wherein,It isJ-th of element value, i.e.,Belong to the probability of each fault category j；
Step 2：Use source domain characteristic extracting module MSIn the parameter of each layer carry out initialized target domain features extraction module MT；
Step 3：Training objective domain features extraction module MT, Location Scale conversion module LS：Input source domain have exemplar and Target domain unlabeled exemplars minimize following distributional difference using back-propagation algorithm：
Estimate target domain characteristic extracting module MTThe parameter of the parameter of each layer and K Location Scale conversion module LS；
Step 4：Fault diagnosis：Target domain fault sampleBy target domain characteristic extracting module MTAfter processing, exports and beIt is calculated by the following formula sampleFault typeThat is the corresponding fault type of most probable value：
Wherein,It isJ-th of element value.
2. the Fault Diagnosis of Roller Bearings under the conditions of variable working condition according to claim 1, it is characterised in that：Institute's rheme The quantity for setting change of scale module LS is consistent with the quantity of fault type.
3. a kind of adaptive deep neural network model of operating mode, it is characterised in that：Including source domain characteristic extracting module MS, failure Grader C, target domain characteristic extracting module MT, Location Scale conversion module LS and field difference regularization module；The source Domain features extraction module MSIncluding 5 layer of 1 dimension convolutional neural networks layer (Conv1~Conv5) and 2 full articulamentums (FC1, FC2), target domain characteristic extracting module MTWith source domain characteristic extracting module MSNetwork structure having the same.
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Cited By (7)

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CN109580215A (en) * 2018-11-30 2019-04-05 湖南科技大学 A kind of wind-powered electricity generation driving unit fault diagnostic method generating confrontation network based on depth
CN110031227A (en) * 2019-05-23 2019-07-19 桂林电子科技大学 A kind of Rolling Bearing Status diagnostic method based on binary channels convolutional neural networks
CN110334478A (en) * 2019-07-22 2019-10-15 山东浪潮人工智能研究院有限公司 Machinery equipment abnormality detection model building method, detection method and model
CN111458142A (en) * 2020-04-02 2020-07-28 苏州智传新自动化科技有限公司 Sliding bearing fault diagnosis method based on generation of countermeasure network and convolutional neural network
CN111538947A (en) * 2020-05-18 2020-08-14 中车永济电机有限公司 Method for constructing wind power generator bearing fault classification model
CN112629863A (en) * 2020-12-31 2021-04-09 苏州大学 Bearing fault diagnosis method for dynamic joint distribution alignment network under variable working conditions
CN115389247A (en) * 2022-11-01 2022-11-25 青岛睿发工程咨询服务合伙企业(有限合伙) Rotating machinery fault monitoring method based on speed self-adaptive encoder

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Publication number Priority date Publication date Assignee Title
CN109580215A (en) * 2018-11-30 2019-04-05 湖南科技大学 A kind of wind-powered electricity generation driving unit fault diagnostic method generating confrontation network based on depth
CN109580215B (en) * 2018-11-30 2020-09-29 湖南科技大学 Wind power transmission system fault diagnosis method based on deep generation countermeasure network
CN110031227A (en) * 2019-05-23 2019-07-19 桂林电子科技大学 A kind of Rolling Bearing Status diagnostic method based on binary channels convolutional neural networks
CN110334478A (en) * 2019-07-22 2019-10-15 山东浪潮人工智能研究院有限公司 Machinery equipment abnormality detection model building method, detection method and model
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CN111458142A (en) * 2020-04-02 2020-07-28 苏州智传新自动化科技有限公司 Sliding bearing fault diagnosis method based on generation of countermeasure network and convolutional neural network
CN111458142B (en) * 2020-04-02 2022-08-23 苏州新传品智能科技有限公司 Sliding bearing fault diagnosis method based on generation of countermeasure network and convolutional neural network
CN111538947A (en) * 2020-05-18 2020-08-14 中车永济电机有限公司 Method for constructing wind power generator bearing fault classification model
CN111538947B (en) * 2020-05-18 2022-06-14 中车永济电机有限公司 Method for constructing wind power generator bearing fault classification model
CN112629863A (en) * 2020-12-31 2021-04-09 苏州大学 Bearing fault diagnosis method for dynamic joint distribution alignment network under variable working conditions
CN115389247A (en) * 2022-11-01 2022-11-25 青岛睿发工程咨询服务合伙企业(有限合伙) Rotating machinery fault monitoring method based on speed self-adaptive encoder

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