Main Reducer Fault Diagnosis Based on Enhanced Multi-scale Differential Symbolic Entropy and Optimized SVM
In order to accurately extract the fault features that can represent the fault of the vehicle's main reducer,and to solve the problems existing in coarse granulating process of the multi-scale differential symbolic entropy(MDSE),the concept of enhanced multi-scale differential symbolic entropy(EMDSE)was proposed.Combined with the support vector machine(SVM)optimized by butterfly optimization algorithm(BOA),a main reducer fault diagnosis method based on EMDSE and BOA-SVM was presented.The EMDSE solves the problems of information leakage and unstable calculation re-sults in the process of MDSE coarse granulating,and can make more effective use of the fault information in the signal.The example results of main reducer fault diagnosis show that,compared with MDSE,the calculation results of EMDSE are more stable and can distinguish different fault states of the main reducer more precisely,and the diagnostic accuracy ob-tained by BOA-SVM is higher.
万方数据
维普
