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Appl. Sci. 2018, 8(9), 1538; https://doi.org/10.3390/app8091538

Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images

1
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, China
2
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
3
School of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, China
4
School of Mechanical-Electrical Engineering, North China Institute of Science and Technology, Yanjiao 101601, China
*
Author to whom correspondence should be addressed.
Received: 21 July 2018 / Revised: 16 August 2018 / Accepted: 21 August 2018 / Published: 3 September 2018
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Abstract

Rolling bearings are important components of rotating machines. For their preventive maintenance, it is not enough to know whether there is any fault or the fault type. For an effective maintenance, a fault severity monitoring needs to be conducted. Currently, the bearing fault diagnosis method based on time–frequency image (TFI) recognition is attracting increasing attention. This paper contributes to the ongoing investigation by proposing a new approach for the fault severity monitoring of rolling bearings based on the texture feature extraction of sparse TFIs. The first and main step is to obtain accurate TFIs from the vibration signals of rolling bearings. Traditional time–frequency analysis methods have disadvantages such as low resolution and cross-term interference. Therefore, the TFIs obtained cannot satisfactorily express the time–frequency characteristics of bearing vibration signals. To solve this problem, a sparse time–frequency analysis method based on the first-order primal-dual algorithm (STFA-PD) was developed in this paper. Unlike traditional time–frequency analysis methods, the time–frequency analysis model of the STFA-PD method is based on the theory of sparse representation, and is solved using the first-order primal-dual algorithm. For employing the sparse constraint in the frequency domain, the STFA-PD obtains a higher time–frequency resolution and is free from cross-term interference, as the model is based on a linear time–frequency analysis method. The gray level co-occurrence matrix is then employed to extract texture features from the sparse TFIs as input features for classifiers. Vibration signals of rolling bearings with different fault severity degrees are used to validate the proposed approach. The experimental results show that the developed STFA-PD outperforms traditional time–frequency analysis methods in terms of the accuracy and effectiveness for the fault severity monitoring of rolling bearings. View Full-Text
Keywords: rolling bearing; vibration signal; fault severity monitoring; sparse time–frequency analysis; gray level co-occurrence matrix; texture feature rolling bearing; vibration signal; fault severity monitoring; sparse time–frequency analysis; gray level co-occurrence matrix; texture feature
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Du, Y.; Chen, Y.; Meng, G.; Ding, J.; Xiao, Y. Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images. Appl. Sci. 2018, 8, 1538.

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