Abstract
Each pattern recognition method has its advantages and disadvantages to diagnose the state of rotating machinery. There are many fault types of rolling bearings with apparent uncertainty. The optimal fusion level is usually challenging to be selected for a specific fault diagnosis task, and extensive human labour and prior knowledge are also highly required during these selections. To solve the above problems, a multimodel decision fusion method based on Deep Convolutional Neural Network and Improved Dempster-Shafer Evidence Theory (DCNN-IDST) is proposed for the inspection of rolling bearing. To solve the defect of the original evidence theory method in the fusion of high-conflict evidence, the fuzzy consistency matrix is introduced. By calculating the factor weight, the reliability and rationality of D-S evidence theory are improved. The DCNN model can learn features from the original data and carry out adaptive feature extraction for multiple sensor information. The features extracted by DCNN adaptively are input into multiple network models for decision fusion. The new method of DCNN-IDST multimodel decision fusion is applied to detect the damage of rolling bearings. To evaluate the effectiveness of the proposed method, both the BP neural network and RBF neural network are used to set up a multigroup comparison test. The result demonstrates that the proposed method can detect the fault of the rolling bearing effectively and achieve the highest diagnosis accuracy among all the tested methods in the experiment.
Highlights
Mechanical fault diagnosis technology is a newly developing subject to monitor, diagnose, and predict the condition and the malfunction of continuously operating mechanical equipment to guarantee its safe operation
It is often challenging to select the optimal feature or fusion level in state monitoring. erefore, combining the advantages of multiple intelligent identification methods, this paper proposes a multimodel decision fusion method based on Deep convolutional neural network (DCNN)-improved method of Dempster–Shafer evidence theory (IDST) for fault diagnosis of rolling bearing
Is research method verifies the feasibility of DCNNIDST-based multimodel decision fusion method in rotating machinery equipment state monitoring by setting up multiple groups of comparative tests. e principal component analysis (PCA) method is used to compare the classification effect of artificial selection feature and DCNN adaptive feature extraction. e feature quantity extracted by artificial feature and the feature quantity removed by DCNN adaptively were input into Backpropagation neural network (BPNN) and Radial Basis Function neural network (RBFNN), respectively, to verify the recognition effect of the model. e diagnosis effect of the new method based on IDST and D-S synthesis formula is proved
Summary
Mechanical fault diagnosis technology is a newly developing subject to monitor, diagnose, and predict the condition and the malfunction of continuously operating mechanical equipment to guarantee its safe operation. It can effectively improve the safety and the stability of rotating machinery equipment [1]. Erefore, combining the advantages of multiple intelligent identification methods, this paper proposes a multimodel decision fusion method based on DCNN-IDST for fault diagnosis of rolling bearing. By this method, the fault information of rotating machinery can be diagnosed comprehensively
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