A Bearing Fault Diagnosis Method Based on Feature Selection Feedback Network and Improved D-S Evidence Fusion

Bearings running state affects the normal operation of mechanical equipment. It is of great theoretical and practical value to carry out bearing fault diagnosis. In bearing fault diagnosis research, the extraction and selection of fault features can help improving the accuracy of bearing fault diagnosis. However, these researches suffer from the following weaknesses. (1) High dimension of the selected features. (2) Uncertainty of single sensor for data sampling. Therefore, in this paper, a feature selection feedback network (FSFN) is proposed to overcome the first weakness. At the same time, we proposed an improved Dempster-Shafer (IDS) evidence theory fusion method based on the kappa coefficient to deal with the second weakness. Extensive evaluations of the proposed method on the CUT-2 experimental platform dataset showed that FSFN can not only reduce the dimension of the final selected feature without decreasing the diagnostic accuracy but also shorten the time of feature selection. Moreover, compared with the existing DS evidence theory fusion method, IDS can achieve higher average fusion precision and improve the accuracy and reliability of bearing fault diagnosis.