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Materials 2017, 10(6), 582; doi:10.3390/ma10060582

Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition

1,2
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3
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2,4,* and 1
1
School of Aeronautic Science and Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing 100191, China
2
Science & Technology on Reliability and Environmental Engineering Laboratory, Xueyuan Road No. 37, Haidian District, Beijing 100191, China
3
China Ship Development and Design Center, Zhang Zhidong Road No. 268, Wuchang District, Wuhan 430064, China
4
School of Reliability and Systems Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Academic Editors: Victor Giurgiutiu and Shenfang Yuan
Received: 15 April 2017 / Revised: 12 May 2017 / Accepted: 16 May 2017 / Published: 25 May 2017
(This article belongs to the Special Issue Structural Health Monitoring for Aerospace Applications 2017)

Abstract

Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP), which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS), we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field. View Full-Text
Keywords: rolling bearing; fault diagnosis; variable conditions; visual cognition; speed up robust feature; isometric mapping rolling bearing; fault diagnosis; variable conditions; visual cognition; speed up robust feature; isometric mapping
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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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Cheng, Y.; Zhou, B.; Lu, C.; Yang, C. Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition. Materials 2017, 10, 582.

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