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Sensors 2014, 14(1), 1295-1321; doi:10.3390/s140101295

A One-Versus-All Class Binarization Strategy for Bearing Diagnostics of Concurrent Defects

Department of Systems Engineering and Engineering Management (SEEM), City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China
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Received: 20 November 2013 / Revised: 25 December 2013 / Accepted: 7 January 2014 / Published: 13 January 2014
(This article belongs to the Section Physical Sensors)
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Abstract

In bearing diagnostics using a data-driven modeling approach, a concern is the need for data from all possible scenarios to build a practical model for all operating conditions. This paper is a study on bearing diagnostics with the concurrent occurrence of multiple defect types. The authors are not aware of any work in the literature that studies this practical problem. A strategy based on one-versus-all (OVA) class binarization is proposed to improve fault diagnostics accuracy while reducing the number of scenarios for data collection, by predicting concurrent defects from training data of normal and single defects. The proposed OVA diagnostic approach is evaluated with empirical analysis using support vector machine (SVM) and C4.5 decision tree, two popular classification algorithms frequently applied to system health diagnostics and prognostics. Statistical features are extracted from the time domain and the frequency domain. Prediction performance of the proposed strategy is compared with that of a simple multi-class classification, as well as that of random guess and worst-case classification. We have verified the potential of the proposed OVA diagnostic strategy in performance improvements for single-defect diagnosis and predictions of BPFO plus BPFI concurrent defects using two laboratory-collected vibration data sets. View Full-Text
Keywords: bearing; multiple defects; fault diagnostics; class binarization; support vector machine (SVM); decision tree bearing; multiple defects; fault diagnostics; class binarization; support vector machine (SVM); decision tree
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Ng, S.S.Y.; Tse, P.W.; Tsui, K.L. A One-Versus-All Class Binarization Strategy for Bearing Diagnostics of Concurrent Defects. Sensors 2014, 14, 1295-1321.

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