International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12
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 Volume 5, Issue 1 (January 2018), Pages: 156-163

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 Original Research Paper

 Title: Extracting accurate time domain features from vibration signals for reliable classification of bearing faults

 Author(s): Muhammad Masood Tahir 1, *, Saeed Badshah 2, Ayyaz Hussain 3, Muhammad Adil Khattak 4

 Affiliation(s):

 1Department of Electrical Engineering, International Islamic University, Islamabad, Pakistan
 2Department of Mechanical Engineering, International Islamic University, Islamabad, Pakistan
 3Department of Computer Science, International Islamic University, Islamabad, Pakistan
 4Faculty of Mechanical Engineering, Universiti Teknologi, Johor, Malaysia

 https://doi.org/10.21833/ijaas.2018.01.021

 Full Text - PDF          XML

 Abstract:

Identification of localized faults in rolling element bearing (REB) frequently utilizes vibration-based pattern recognition (PR) methods. Time domain (TD) statistical features are often part of the diagnostic models. The extracted statistical values are, however, influenced by the fluctuations present in random vibration signals. These inaccurate values consequently affect the diagnostic capability of the supervised learning based classifiers. This study examines the sensitivity of TD features to signal fluctuations. Vibration data is acquired from different REBs containing localized faults using a test rig, and a central tendency (CT) based feature extraction (CTBFE) method is proposed. The CTBFE ensures the supply of reliable feature values to the PR models. The method selects the fault related appropriate portion of a vibration signal prior to extract TD features. Variety of classifiers is used to judge the effect of CTBFE method on their fault classification accuracies, which are enhanced considerably. The results are also compared with a similar sort of existing method, where the proposed method provides better results and feasibility for on-line applications. 

 © 2017 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Pattern recognition, Feature extraction, Time domain features, Fault diagnosis, Central tendency, Vibration analysis

 Article History: Received 20 August 2017, Received in revised form 10 November 2017, Accepted 1 December 2017

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2018.01.021

 Citation:

 Tahir MM, Badshah S, Hussain A, and Khattak MA (2018). Extracting accurate time domain features from vibration signals for reliable classification of bearing faults. International Journal of Advanced and Applied Sciences, 5(1): 156-163

 Permanent Link:

 http://www.science-gate.com/IJAAS/2018/V5I1/Tahir.html

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