Bearing fault diagnosis under variable speed conditions on adaptive time frequency extraction mode decomposition

Abstract

Improvements in measurement technology have made it possible to detect problems with rolling bearings more accurately, which is important to ensure that they work properly in mechanical systems under different variable speed conditions. Time–frequency distribution (TFD) methods are widely used in variable-speed rolling bearing fault diagnosis, we construct a new method: adaptive time frequency extraction mode decomposition (ATFEMD) by capturing the distinctive time–frequency information within the TFD through ridge extraction, subsequently, the reconstruction components are further refined into adaptive modes through the harmonic detection and noise testing process. This method is a time–frequency post-processing method that effectively solves the problems of time–frequency energy lack of concentration, poor robustness of instantaneous frequency extraction, and mode aliasing in signal decomposition. This article analyzes the simulated bearing vibration and test bench bearing vibration signals to demonstrate the performance of ATFEMD. Results indicated that the proposed method is characterized by strong robustness, and good feature extraction results compared to other methods.