A new fault diagnosis approach for bearing based on multi-scale entropy of the optimized VMD
(2.Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, Guangdong Province, China 525000)
【Abstract】It is well known that the parameter K of variational mode decomposition (VMD) needs to be preset according to prior knowledge without theoretical support for optimal setting. Thus, for the existing fault diagnosis methods of bearings based on VMD, the correctness of characteristic extraction and accuracy of fault diagnosis are extremely difficult to be guaranteed. A novel collaborative diagnosis approach of petrochemical equipment for bearing based on optimal VMD and multiscale entropy (MSE) is proposed to solve this problem. Firstly, because of the difficulty in optimizing the decomposition parameter K of VMD, an effective estimation model of K is constructed according to the frequency distribution characteristics of the decomposition components of local mean decomposition (LMD). Then, a novel characteristic extraction technique combining MSE with linear discriminant analysis (LDA) is proposed to establish characteristic samples. Furthermore, aimed at the fault features of small samples for bearing, support vector machine (SVM) is introduced to identify the fault characteristics. Finally, the bearing fault data collected from the simulation platform of the petrochemical equipment laboratory is used to verify the effectiveness and engineering practicability of the proposed approach. The comparative analyses show that the proposed algorithm can effectively diagnose the faults of the bearing with good engineering operability and scalability.
【Keywords】 bearing faults; VMD algorithm; MSE algorithm; LDA algorithm; SVM fault characteristics recognition;
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