Squared Envelope Sparsification via Blind Deconvolution and its Application to Railway Axle Bearing Diagnostics

Bingyan Chen, Weihua Zhang, Dongli Song, Yao Cheng, Fengshou Gu, Andrew Ball

Research output: Contribution to journalArticlepeer-review

Abstract

A sparse squared envelope is crucial for efficient and accurate diagnosis of bearing faults. Blind deconvolution is a well-established sparse feature enhancement method for the diagnostics of rolling bearings. Traditional blind deconvolution methods, such as minimum entropy deconvolution, are susceptible to random transients, making it difficult to enhance fault features of rolling bearings subject to strong random shocks. Deconvolution methods that take the fault characteristic frequency (or fault impulse period) of interest as an algorithm input parameter, such as maximum second-order cyclostationarity blind deconvolution, can alleviate this deficiency. However, bearing fault features are difficult to be enhanced by these methods when the specified characteristic frequency deviates from the actual value greatly. To overcome these problems, the modified smoothness index of the squared envelope is proposed as the objective function of the deconvolution method, and a new blind deconvolution method is developed to achieve a sparse squared envelope for fault diagnosis of rolling bearings. Furthermore, the methodology is extended to the frequency domain, and another new blind deconvolution method that utilizes the modified smoothness index of the squared envelope spectrum as the objective function is established to achieve a sparse squared envelope spectrum for bearing diagnostics. These two proposed blind deconvolution methods are robust to random transients and do not require characteristic frequency or impulse period as an input parameter for feature enhancement. The performance of the two proposed blind deconvolution methods is verified on experimental datasets from two different railway axle bearing test rigs and compared with the state-of-the-art deconvolution methods. The results show that the two proposed methods can effectively enhance repetitive transient features in noisy vibration signals and accurately diagnose different faults of railway axle bearings.
Original languageEnglish
JournalStructural Health Monitoring
Publication statusAccepted/In press - 15 Dec 2022

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