Intelligent Bearing Fault Diagnosis Based on Tacholess Order Tracking for a Variable-Speed AC Electric Machine

Abstract

Bearing fault diagnosis in an alternating-current (AC) electric machine (EM) under variable-speed conditions without a tachometer is a challenge. This issue is addressed through an intelligent method based on synchrosqueezing wavelet transform (SWT) and tacholess order tracking (TOT) techniques, which are used to analyze the synchronously sampled current and vibration signals, respectively. The current signal is typically an amplitude- and frequency-modulated (AM–FM) signal. SWT can not only extract the instantaneous frequencies of the AM–FM signal but also reconstruct the harmonic components accurately. Initially, the IF curve with the highest energy is extracted adaptively to reconstruct the rotation component. Subsequently, the mechanical rotation angle is calculated from the rotation component for TOT. The effectiveness of the proposed method is evaluated on a brushless direct-current motor and a permanent magnet synchronous generator with different fault bearings. The method’s superiority is verified through a comparative study. Given that the proposed method is intelligent and efficient, it can be used in the industry and extended to the fault diagnosis of other AC EMs.