Bearing fault diagnosis of rail vehicles traction motor based on adaptive periodic segment matrix and singular value decomposition

Abstract

The early fault characteristics of rolling bearings are relatively weak and accompanied by large amounts of noise, resulting in the collected bearing vibration signal having an extremely low signal-to-noise ratio, hence creating challenges in diagnosing faults in rail vehicle bearings. This paper proposes a method based on an adaptive period segment matrix and singular value decomposition to extract fault information from bearing fault impulse signals, in response to the problem of unstable period recognition in existing rolling bearing fault diagnosis methods due to strong background noise. Firstly, a periodic segment estimation method based on the average envelope spectrum is proposed. By calculating the envelope spectrum of the vibration signal, a method is proposed for assessing period length and period constraint F in segments. Then this method is applied to pinpoint the fault impact peak and the position of the vibration signal. Utilizing the fault impact peak and the calculated period constraint F, a strict rank-1 adaptive period segment matrix (APSM) is introduced. Finally, singular value decomposition is performed on this matrix, the bearing impulse vibration signal can be reconstructed through the first maximum singular value. The simulation and experimental analysis results of rolling bearings signals demonstrate the performance of the proposed method, even under severe background noise conditions (-20 dB), and the calculated envelope spectrum signal-to-noise ratio further supports the adaptive periodic segment matrix has an excellent ability to identify and extract periodic components, thus validating its usefulness as a fault diagnosis approach for rolling bearings.