A Method Of Rotating Machinery Fault Diagnosis Based On Deep Learning Theory

The traditional intelligent diagnosis method is based on the "feature extraction add classifier" model,the core of the method are the extraction of feature value and the design of classifier.For the different diagnosis objects,it is usually necessary to extract different fault eigenvalues according to the prior knowledge,which will inevitably bring diagnostic errors to the final diagnosis results.At the same time,the traditional classifier uses the shallow models generally.This makes it difficult to characterize the complex mapping relationship between the signal and the equipment operation.In this context,Hinton and his colleagues put forward the concept of deep learning.Compared with the shallow model established by the traditional artificial intelligence diagnosis method,the deep learning theory is devoted to learning the hidden features of the data by simulating the learning process of the brain,establishing the deep learning model and combining the massive training data.Thus the complex mapping relationship between signal and health status is established.As a typical representative of the deep learning algorithm,the deep convolution neural network(DCNN)and the deep belief network break away from the dependence on the signal processing technology and the expert knowledge by establishing the deep learning model.Thus,the adaptive extraction of fault features and the intelligent diagnosis of health condition can be completed.Supported by National Natural Science Foundation(No.51575168),the deep learning method is introduced to the fault diagnosis of rotating machinery in this dissertation.The main research contents of this dissertation are listed as follows:(1)The application of VMD in fault diagnosis of rotating machinery is discussed,and the variational mode decomposition method is studied emphatically.In order to solve the problem that it is necessary to determine the number of penalty function and component decomposition according to prior knowledge in practical application,an improved algorithm,generalized variational mode decomposition(GVMD),is proposed in this dissertation.The subjective influence of human factors on the decomposition results can be reduced in this method,and the signal can be resolved into non-recursive and variational mode,which can separate harmonic components with similar frequency components effectively.And it has good robustness to the signal with low signal-to-noise ratio(SNR).The effectiveness of the proposed method verified by the results of simulation and experimental signal analysis.(2)By establishing a deep learning model,DCNN gets rid of the dependence on signal processing technology and expert knowledge.But,as a big data processing tool,the diagnostic accuracy will be affected when the number of training samples is small.However,the number of labeled sample signals we can obtain in engineering practice is limited.In order to make full use of the ability of DC NN adaptive feature extraction,on the basis of DCNN,the energy function model of intra-class and inter-class constraints are introduced by the deep convolution neural network recognition algorithm based on Fisher criterion(FDCNN).It can be applied to feature extraction and fault diagnosis in small samples.However,the model parameters in this method need to be selected artificially,which will inevitably affect the diagnosis results.In this dissertation,an adaptive Fisher criterion for deep convolution neural networks(AFDCNN)is proposed.In this method,the parameters of the optimal energy function model are obtained by using the optimization algorithm,and then the fault features are automatically extracted from the original time domain signal,and finally the fault diagnosis is realized.The analysis of gear fault signals and bearing fault signals show that this method can effectively realize the intelligent and quantitative fault diagnosis of rotating machinery with small samples.(3)As one of the typical representatives of the deep learning algorithm,the deep belief network can form abstract high-level representation by combining the lower level features,and then the distributed features of the data can be discovered.At the same time,there is no need for signal reconstruction when the obtained vibration signal is input into the DBN model.As a result,more and more researchers have paid their attention to it.However,DBN still has the defect that the network structure needs to be set artificially,which limits the application of DBN in engineering practice.In order to solve the problem of how to determine the structure of DBN network and how to improve its diagnostic efficiency in engineering practice,a new kind of depth belief network,namely structural adaptive deep belief network,is proposed in this dissertation.Compared with DBN,SADBN takes the entropy error of network structure and calculation cost as the index to determine the optimized network structure and improve the efficiency of diagnosis.The results of vibration signal analysis of gear fault and rolling bearing fault show that the improved network is effective.(4)In order to solve the problem of complex fault feature extraction of rotating machinery,the AFDCNN and SADBN methods are introduced into the complex fault diagnosis of rotating machinery in this dissertation.The method of complex fault diagnosis of rotating machinery based on AFDCNN method and the method of complex fault diagnosis of rotating machinery based on SADBN method are presented.Based on the AFDCNN and SADBN methods,the corresponding si mulation and experimental models are designed for the gearbox.The effectiveness of the two methods in the complex fault diagnosis of rotating machinery is verified by simulation and experimental analysis.