Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising

Jie Gao,Lifeng Wu,Hongmin Wang,Yong Guan

doi:10.3390/app6050154

Abstract

Singular value decomposition (SVD) is a widely used and powerful tool for signal extraction under noise. Noise attenuation relies on the selection of the effective singular value because these values are significant features of the useful signal. Traditional methods of selecting effective singular values (or selecting the useful components to rebuild the faulty signal) consist of seeking the maximum peak of the differential spectrum of singular values. However, owing to the small number of selected effective singular values, these methods lead to excessive de-noised effects. In order to get a more appropriate number of effective singular values, which preserves the components of the original signal as much as possible, this paper used a difference curvature spectrum of incremental singular entropy to determine the number of effective singular values. Then the position was found where the difference of two peaks in the spectrum declines in an infinitely large degree for the first time, and this position was regarded as the boundary of singular values between noise and a useful signal. The experimental results showed that the modified methods could accurately extract the non-stationary bearing faulty signal under real background noise.

Highlights

Rolling bearings are vital components of rotating machinery systems
Aiming at the problem narrated above, this paper presents a method that integrates the difference of curvature peaks with incremental singular entropy
The traditional methods of eliminating noise require prior knowledge of the signal and a lot of parameters to be set, a complicated iteration in the process of optimization may cause a decrease in real-time fault detection

Summary

Introduction

Rolling bearings are vital components of rotating machinery systems. With the development of rotating machinery devices, much attention has been focused on bearing fault diagnosis as a means of ensuring the safe operation of rotating machinery systems [1]. Catastrophic failure often evolves from a single early fault that spreads through the system. If a fault can be detected as early as possible, many disastrous accidents could be avoided. In order to ensure the reliable operation of a system, one must identify faults before deterioration occurs. One barrier to early detection of faults in bearings is the difficulty of extracting a weak characteristic fault signal submerged in strong background noise.

Methods

Discussion

Conclusion