research-article

Fault diagnosis based on dependent feature vector and probability neural network for rolling element bearings

Authors:

Jianzhong Zhou,

Wenlong FuAuthors Info & Claims

Applied Mathematics and Computation, Volume 247, Issue C

Pages 835 - 847

https://doi.org/10.1016/j.amc.2014.09.062

Published: 15 November 2014 Publication History

Abstract

Rolling element bearings (REB) are crucial mechanical parts of most rotary machineries, and REB failures often cause terrible accidents and serious economic losses. Therefore, REB fault diagnosis is very important for ensuring the safe operation of rotary machineries. In previous researches on REB fault diagnosis, achieving the accurate description of faults has always been a difficult problem, which seriously restricts the reliability and accuracy of the diagnosis results. In order to improve the precision of fault description and provide strong basis for fault diagnosis, dependent feature vector (DFV) is proposed to denote the fault symptom attributes of the six REB faults in this paper, and this is a self-adaptive fault representation method which describes each fault sample according to its own characteristics. Because of its unique feature selection technique and particular structural property, DFV is excellent in fault description, and could lay a good foundation for fault diagnosis. The advantages of DFV are theoretically proved via the Euclidean distance evaluation technique. Finally, a fault diagnosis method combining DFV and probability neural network (PNN) is proposed and applied to 708 REB fault samples. The experimental results indicate that the proposed method can achieve an efficient accuracy in REB fault diagnosis.

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Cited By

(2019)An effective feature selection method based on maximum class separability for fault diagnosis of ball bearingInternational Journal of Data Analysis Techniques and Strategies10.5555/3337530.333753211:2(115-132)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.5555/3337530.3337532
Lahmiri S(2018)Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regressionApplied Mathematics and Computation10.1016/j.amc.2017.09.049320:C(444-451)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.amc.2017.09.049
Seera MWong MNandi A(2017)Classification of ball bearing faults using a hybrid intelligent modelApplied Soft Computing10.1016/j.asoc.2017.04.03457:C(427-435)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.asoc.2017.04.034
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Fault diagnosis based on dependent feature vector and probability neural network for rolling element bearings
1. Computing methodologies
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Published In

cover image Applied Mathematics and Computation

Applied Mathematics and Computation Volume 247, Issue C

November 2014

1210 pages

ISSN:0096-3003

Issue’s Table of Contents

Copyright © Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 15 November 2014

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Cited By

(2019)An effective feature selection method based on maximum class separability for fault diagnosis of ball bearingInternational Journal of Data Analysis Techniques and Strategies10.5555/3337530.333753211:2(115-132)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.5555/3337530.3337532
Lahmiri S(2018)Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regressionApplied Mathematics and Computation10.1016/j.amc.2017.09.049320:C(444-451)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.amc.2017.09.049
Seera MWong MNandi A(2017)Classification of ball bearing faults using a hybrid intelligent modelApplied Soft Computing10.1016/j.asoc.2017.04.03457:C(427-435)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.asoc.2017.04.034
Du D(2017)Fault detection for discrete-time linear systems based on descriptor observer approachApplied Mathematics and Computation10.1016/j.amc.2016.08.052293:C(575-585)Online publication date: 15-Jan-2017
https://dl.acm.org/doi/10.1016/j.amc.2016.08.052
Lu CWang ZZhou B(2017)Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classificationAdvanced Engineering Informatics10.1016/j.aei.2017.02.00532:C(139-151)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1016/j.aei.2017.02.005

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