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Fuzzy support tensor train machine and its application in rolling bearing fault diagnosis
Published:2022-12-20 author:WANG Jin-feng, XUE Yu-shi, SHAN Chun-feng. Browse: 1138 Check PDF documents
Fuzzy support tensor train machine and its application 
in rolling bearing fault diagnosis


WANG Jin-feng1, XUE Yu-shi2, SHAN Chun-feng3


(1.YangZhou Technician Branch of JiangSu Union Technical Institute, YangZhou 225000, China; 2.State Machinery 

Precision Co., Ltd., Zhengzhou 450142, China; 3.Luoyang Bearing Research Institute Co., Ltd., Luoyang 471039, China)


Abstract:  Aiming at the problem that support vector machine (SVM) cannot protect the characteristic information of fault signal when modeling of sample imbalanced data, a fuzzy support tensor train machine (FSTTM) method was proposed in this paper.Firstly,in FSTTM, tensor samples were constructed by using multisource fault signals, and tensor train (TT) decomposition method was introduced into the model to extract the feature information contained in highorder tensor samples. Then, the prediction model based on TT kernel function was established, which can solve the classification problem of nonlinear data. Finally, a fuzzy factor was designed in the objective function, which can make the tendency equilibrium of the type of sample with less number and the type with more number of samples, and realize the effective classification of sample unbalanced data. Two different roller bearing data were used for experimental analysis of fault diagnosis. The results show that the fault identification accuracy of FSTTM is more than 97% and the F1 score index is more than 0.9800. Compared with the traditional support tensor machine, highorder tensor and fuzzy factor are used FSTTM to construct the prediction model, which can make full use of the original signal state information and accurately classify the sample unbalanced data.

Key words: characteristic information of fault signal; fuzzy support tensor train machine(FSTTM); tensor train(TT) decomposition method; support vector machine(SVM); sample imbalance modeling; multisource fault signals; model classification performance

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