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Sensors 2016, 16(9), 1524;

A Sparsity-Promoted Decomposition for Compressed Fault Diagnosis of Roller Bearings

College of Mechanical & Electrical Engineering, Beijing University of Chemical Technology Chao Yang District, Beijing 100029, China
Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu, Mie 514-8507, Japan
Authors to whom correspondence should be addressed.
Academic Editor: Gangbing Song
Received: 25 July 2016 / Revised: 13 September 2016 / Accepted: 14 September 2016 / Published: 19 September 2016
(This article belongs to the Section Physical Sensors)


The traditional approaches for condition monitoring of roller bearings are almost always achieved under Shannon sampling theorem conditions, leading to a big-data problem. The compressed sensing (CS) theory provides a new solution to the big-data problem. However, the vibration signals are insufficiently sparse and it is difficult to achieve sparsity using the conventional techniques, which impedes the application of CS theory. Therefore, it is of great significance to promote the sparsity when applying the CS theory to fault diagnosis of roller bearings. To increase the sparsity of vibration signals, a sparsity-promoted method called the tunable Q-factor wavelet transform based on decomposing the analyzed signals into transient impact components and high oscillation components is utilized in this work. The former become sparser than the raw signals with noise eliminated, whereas the latter include noise. Thus, the decomposed transient impact components replace the original signals for analysis. The CS theory is applied to extract the fault features without complete reconstruction, which means that the reconstruction can be completed when the components with interested frequencies are detected and the fault diagnosis can be achieved during the reconstruction procedure. The application cases prove that the CS theory assisted by the tunable Q-factor wavelet transform can successfully extract the fault features from the compressed samples. View Full-Text
Keywords: roller bearing; fault diagnosis; compressed sensing; sparsity; fault feature; tunable Q-factor wavelet transform roller bearing; fault diagnosis; compressed sensing; sparsity; fault feature; tunable Q-factor wavelet transform

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Wang, H.; Ke, Y.; Song, L.; Tang, G.; Chen, P. A Sparsity-Promoted Decomposition for Compressed Fault Diagnosis of Roller Bearings. Sensors 2016, 16, 1524.

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