Multifault Diagnosis of Rolling Element Bearings Using a Wavelet Kurtogram and Vector Median-Based Feature Analysis
Joint Authors
Nguyen, Phuong H.
Kim, Jong-Myon
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-13
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
This paper presents a comprehensive multifault diagnosis methodology for incipient rolling element bearing failures.
This is done by combining a wavelet packet transform- (WPT-) based kurtogram and a new vector median-based feature analysis technique.
The proposed approach first extracts useful features that are characteristic of the bearing health condition from the time domain, frequency domain, and envelope power spectrum of incoming acoustic emission (AE) signals by using a WPT-based kurtogram.
Then, an enhanced feature analysis approach based on the linear discriminant analysis (LDA) technique is used to select the most discriminant bearing fault features from the original feature set.
These selected fault features are used by a Naïve Bayes (NB) classifier to classify the bearing fault conditions.
The performance of the proposed methodology is tested and validated under various bearing fault conditions on an experimental test rig and compared with conventional state-of-the-art approaches.
The proposed bearing fault diagnosis methodology yields average classification accuracies of 91.11%, 96.67%, 98.89%, 99.44%, and 98.61% at rotational speeds of 300, 350, 400, 450, and 500 rpm, respectively.
American Psychological Association (APA)
Nguyen, Phuong H.& Kim, Jong-Myon. 2015. Multifault Diagnosis of Rolling Element Bearings Using a Wavelet Kurtogram and Vector Median-Based Feature Analysis. Shock and Vibration،Vol. 2015, no. 2015, pp.1-14.
https://search.emarefa.net/detail/BIM-1078063
Modern Language Association (MLA)
Nguyen, Phuong H.& Kim, Jong-Myon. Multifault Diagnosis of Rolling Element Bearings Using a Wavelet Kurtogram and Vector Median-Based Feature Analysis. Shock and Vibration No. 2015 (2015), pp.1-14.
https://search.emarefa.net/detail/BIM-1078063
American Medical Association (AMA)
Nguyen, Phuong H.& Kim, Jong-Myon. Multifault Diagnosis of Rolling Element Bearings Using a Wavelet Kurtogram and Vector Median-Based Feature Analysis. Shock and Vibration. 2015. Vol. 2015, no. 2015, pp.1-14.
https://search.emarefa.net/detail/BIM-1078063
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1078063