Rolling Bearing Weak Fault Feature Extraction under Variable Speed Conditions via Joint Sparsity and Low-Rankness in the Cyclic Order-Frequency Domain

Ran Wang,Chenyu Zhang,Haitao Fang,Liang Yu,Xiong Hu

doi:10.3390/app12052449

Ran Wang, Chenyu Zhang + Show 3 more

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https://doi.org/10.3390/app12052449

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Abstract

Rolling bearings are critical to the normal operation of mechanical systems, which often undergo time-varying working conditions. When the local defects appear on a rolling bearing, the transient impulses will generate and be covered by the strong background noise. Therefore, extracting the rolling bearing weak fault feature with time-varying speed is critical to mechanical system diagnosis. A weak fault feature extraction strategy of rolling bearing under time-varying working conditions is proposed. Firstly, the order-frequency spectral correlation (OFSC) is computed for transferring the measured signal into a higher dimensional space. Then, the joint sparsity and low-rankness constraint is imposed on OFSC to detect the time-varying faulty characteristics. An algorithm in the alternating direction method of multipliers (ADMM) framework is derived. Finally, the enhanced envelope order spectrum (EEOS) is applied to further detect the defective features, which can make the fault features more obvious. The feasibility of the proposed method is confirmed by simulations and an experimental case.

Highlights

Rolling bearings are among the most vital rotating elements in a mechanical system, which may be a direct failure for machines breakdown [4]
The vibration-based analysis can be used for the rolling bearing fault diagnosis, and the fault feature extraction task aims to extract the feature of weak transient impulses from the noisy signal, which is valuable for fault diagnosis [8–10]
Compared with the other two methods, the noise components in order-frequency spectral correlation (OFSC) are still greater than the denoised OFSC obtained by the joint sparsity and low-rankness constraint

Summary

Introduction

Rolling bearings are among the most vital rotating elements in a mechanical system (wind turbine [1], centrifuges, helicopters, washing machines, and so on [2,3]), which may be a direct failure for machines breakdown [4]. When rolling bearings are in normal operation, the vibration will generate, caused by raceway waviness, radial play, friction and so on [5,6]. Once the local defects occur, the transient impulses in vibration signals will generate (quite different from the vibration signals in normal operation), covered by a strong background noise [7]. When the rotating speed is stationary, several methods can be used to identify the stable fault characteristic frequency (FCF) to determine the types of faults (inner race, outer race and rollers). Different techniques have been developed to identify the fault types according to different FCFs, like envelope analysis [11] (shift the higher resonance frequency band into a lower fault frequency band to achieve a higher resolution), wavelet analysis [12] (denoise the vibration signal via wavelet decomposition), spectral kurtosis [13,14]

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