Research Papers

Incipient Bearing Fault Feature Extraction Based on Minimum Entropy Deconvolution and K-Singular Value Decomposition

[+] Author and Article Information
Guangming Dong

State Key Laboratory of Mechanical
Systems and Vibration,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: gmdong@sjtu.edu.cn

Jin Chen

State Key Laboratory of Mechanical
Systems and Vibration,
Shanghai Jiao Tong University,
Shanghai 200240, China

Fagang Zhao

Shanghai Institute of Satellite Engineering,
251 Huaning Road,
Minhang District,
Shanghai 200240, China

Manuscript received January 13, 2017; final manuscript received June 29, 2017; published online August 24, 2017. Assoc. Editor: Ivan Selesnick.

J. Manuf. Sci. Eng 139(10), 101006 (Aug 24, 2017) (12 pages) Paper No: MANU-17-1020; 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 predefined dictionaries. Analysis on simulated bearing signals and real signals shows that K-SVD can give better bearing fault features than the predefined 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 prefilter. 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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Fig. 1

The process of dictionary learning

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Fig. 2

Input, output, and objective of K-SVD

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Fig. 3

Flowchart of the K-SVD algorithm

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Fig. 4

The simulation bearing signal with inner race fault

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Fig. 5

Nine randomly selected atoms from the learned dictionary

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Fig. 6

The denoised signal using K-SVD method

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Fig. 7

The effect of noise on reconstruction accuracy

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Fig. 8

The effect of total number of atoms on K-SVD: (a) reconstruction accuracy and (b) computation time

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Fig. 9

The comparison of approximation error between K-SVD dictionary and predefined dictionaries

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Fig. 10

Bearing fault feature extraction based on MED and K-SVD

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Fig. 11

The original simulated signal: (a) time waveform and (b) envelope frequency spectrum

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Fig. 12

The denoised signal using MED: (a) time waveform and (b) envelope frequency spectrum

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Fig. 13

The denoised signal using MED and K-SVD: (a) time waveform and (b) envelope frequency spectrum

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Fig. 14

The denoised signal using K-SVD only: (a) time waveform and (b) envelope frequency spectrum

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Fig. 15

The denoised signal using SK filter: (a) time waveform and (b) envelope frequency spectrum

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Fig. 16

Testing object: (a) locations of acceleration sensors and (b) the sketch of sensors installations

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Fig. 17

Experiment equipment

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Fig. 18

Failure on rolling element bearing

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Fig. 19

The root-mean-square (RMS) value during the whole life of the tested bearing

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Fig. 20

The time waveform and its frequency spectrum in each stage: (a) and (b) normal stage; (c) and (d) incipient fault stage; (e) and (f) severe fault stage

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Fig. 21

The original signal at 1877th min: (a) time waveform and (b) frequency spectrum

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Fig. 22

The filtered signal using MED: (a) time waveform and (b) frequency spectrum

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Fig. 23

Nine atoms in K-SVD dictionary

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Fig. 24

The denoised signal using MED and K-SVD: (a) time waveform and (b) envelope frequency spectrum

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Fig. 25

The denoised signal using SK filter: (a) time waveform and (b) envelope frequency spectrum

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Fig. 26

Analysis results using predefined dictionary Db8: (a) denoised signal using MP, (b) envelope spectrum of (a), (c) denoised signal using BP, and (d) envelope spectrum of (c)




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