Next Article in Journal
A Survey of Routing Protocols in Wireless Body Sensor Networks
Previous Article in Journal
Comparison of Piezoresistive Monofilament Polymer Sensors
Sensors 2014, 14(1), 1295-1321; doi:10.3390/s140101295
Article

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

* ,
 and
Received: 20 November 2013; in revised form: 25 December 2013 / Accepted: 7 January 2014 / Published: 13 January 2014
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3838 KB, updated 21 June 2014; original version uploaded 21 June 2014]   |   Browse Figures
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.
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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


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.

AMA Style

Ng SSY, Tse PW, Tsui KL. A One-Versus-All Class Binarization Strategy for Bearing Diagnostics of Concurrent Defects. Sensors. 2014; 14(1):1295-1321.

Chicago/Turabian Style

Ng, Selina S.Y.; Tse, Peter W.; Tsui, Kwok L. 2014. "A One-Versus-All Class Binarization Strategy for Bearing Diagnostics of Concurrent Defects." Sensors 14, no. 1: 1295-1321.


Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert