Incipient bearing fault feature extraction based on minimum entropy deconvolution and K-SVD

[+] Author and Article Information
Guangming Dong

State Key Laboratory of Mechanical Systems & Vibration, Shanghai Jiao Tong University, China

Jin Chen

State Key Laboratory of Mechanical Systems & Vibration, Shanghai Jiao Tong University, China

Fagang Zhao

Shanghai Institute of Satellite Engineering, 251 Huaning RD. Minhang District, Shanghai, China

1Corresponding author.

ASME doi:10.1115/1.4037419 History: Received January 13, 2017; Revised June 29, 2017


Machinery condition monitoring and fault diagnosis are essential for early detection of equipment malfunctions or failures, which insure productivity, quality and safety in the manufacturing process. This paper aims at extracting fault features of rolling element bearings at the incipient fault stage. K-singular value decomposition (K-SVD), one technique for sparse representation of signals, is used for study. In K-SVD, its dictionary is trained from data by machine learning techniques, which allows more flexibility to adapt to variation of real signals than the pre-defined dictionaries. Analysis on simulated bearing signals and real signals show that K-SVD can give better bearing fault features than the pre-defined dictionaries such as wavelet dictionaries. However, during our simulation study, K-SVD was found to have large representation error under heavy noise. To reduce the noise effect, minimum entropy deconvolution (MED) is used as a pre-filter. The combination of MED and K-SVD is proposed for incipient bearing fault detection. The method is verified by simulation and experimental study. It is shown that the proposed method can effectively extract the impulsive fault feature of the tested bearing at its incipient fault stage.

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