Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm
Kang, Myeongsu, Kim, Jaeyoung, Kim, Jong-Myon, Tan, Andy C.C., Kim, Eric Y., & Choi, Byeong-Keun (2015) Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm. Information Sciences, 294, pp. 423-438.
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In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20 revolutions-per-minute (RPM), 80 RPM, and 140 RPM, respectively.
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|Item Type:||Journal Article|
|Additional Information:||Corrigendum from publisher: "Andy C.C. Tan and Eric Y.H. Kim should have been included as authors of the paper starting on page 423 of this volume, because the data and images (Figs. 1 and 2) presented originated from a low-speed machinery fault simulator, developed under the leadership of Andy Tan at CRC IEAM, Queensland University of Technology, Australia. The correct author names and affiliations are given above." http://www.sciencedirect.com/science/article/pii/S0020025515002662|
|Keywords:||Acoustic emission, Binary bat Algorithm, Dimensionality reduction, Incipient low-speed bearing fault diagnosis, Multiclass support vector machines, Wavelet packet transform|
|Divisions:||Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2014 Elsevier Inc.|
|Copyright Statement:||This is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, [VOL 294, (2015)] DOI: 10.1016/j.ins.2014.10.014|
|Deposited On:||15 May 2015 04:20|
|Last Modified:||31 May 2015 04:45|
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