Yang Xu
1.Beijing Key Laboratory of High Dynamic Navigation Technology，Beijing Information Science & Technology University， Beijing 100101,China； 2.Key Laboratory of Modern Measurement & Control Technology, Ministry of Education，Beijing Information Science & Technology University，Beijing 100101,ChinaZhang Tao
1.Beijing Key Laboratory of High Dynamic Navigation Technology，Beijing Information Science & Technology University， Beijing 100101,China； 2.Key Laboratory of Modern Measurement & Control Technology, Ministry of Education，Beijing Information Science & Technology University，Beijing 100101,ChinaLi Yumei
1.Beijing Key Laboratory of High Dynamic Navigation Technology，Beijing Information Science & Technology University， Beijing 100101,China； 2.Key Laboratory of Modern Measurement & Control Technology, Ministry of Education，Beijing Information Science & Technology University，Beijing 100101,ChinaLiu Hong
1.Beijing Key Laboratory of High Dynamic Navigation Technology，Beijing Information Science & Technology University， Beijing 100101,China； 2.Key Laboratory of Modern Measurement & Control Technology, Ministry of Education，Beijing Information Science & Technology University，Beijing 100101,China1.Beijing Key Laboratory of High Dynamic Navigation Technology，Beijing Information Science & Technology University， Beijing 100101,China; 2.Key Laboratory of Modern Measurement & Control Technology, Ministry of Education，Beijing Information Science & Technology University，Beijing 100101,China
TH113
Aiming at the problems of motor bearings being prone to failure, the traditional fault diagnosis method has long time, low diagnostic accuracy and many adjustment parameters, and this paper proposes a bearing fault diagnosis method for support vector machine SVM optimized by improving sparrow algorithm ISSA. The classification algorithm introduces improved Tent chaos mapping, flock algorithm random following strategy, adaptive t distribution and dynamic selection strategy in the traditional sparrow optimization algorithm, and first uses CEEMDANenergy entropy to decompose the vibration signal, selects the energy entropy values of the five IMF components with the greatest correlation with the original signal as the eigenvector, and then inputs it to the ISSASVM classifier for bearing fault diagnosis. Experimental comparison with PSOSVM、GWOSVM and SSASVM classification models shows that the diagnostic accuracy of the ISSASVM diagnostic model can reach up to 100%.
