Extracting accurate time domain features from vibration signals for reliable classification of bearing faults
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 prese...
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2018
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my.utm.856412020-07-07T05:00:07Z http://eprints.utm.my/id/eprint/85641/ Extracting accurate time domain features from vibration signals for reliable classification of bearing faults Tahir, Muhammad Masood Badshah, Saeed Hussain, Ayyaz Khattak, Muhammad Adil TJ Mechanical engineering and machinery 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. Institute of Advanced Engineering and Science 2018 Article PeerReviewed Tahir, Muhammad Masood and Badshah, Saeed and Hussain, Ayyaz and Khattak, Muhammad Adil (2018) Extracting accurate time domain features from vibration signals for reliable classification of bearing faults. International Journal of Advanced and Applied Sciences, 5 (1). pp. 156-163. ISSN 2313-626X http://dx.doi.org/10.21833/ijaas.2018.01.021 |
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TJ Mechanical engineering and machinery Tahir, Muhammad Masood Badshah, Saeed Hussain, Ayyaz Khattak, Muhammad Adil Extracting accurate time domain features from vibration signals for reliable classification of bearing faults |
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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. |
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Article |
author |
Tahir, Muhammad Masood Badshah, Saeed Hussain, Ayyaz Khattak, Muhammad Adil |
author_facet |
Tahir, Muhammad Masood Badshah, Saeed Hussain, Ayyaz Khattak, Muhammad Adil |
author_sort |
Tahir, Muhammad Masood |
title |
Extracting accurate time domain features from vibration signals for reliable classification of bearing faults |
title_short |
Extracting accurate time domain features from vibration signals for reliable classification of bearing faults |
title_full |
Extracting accurate time domain features from vibration signals for reliable classification of bearing faults |
title_fullStr |
Extracting accurate time domain features from vibration signals for reliable classification of bearing faults |
title_full_unstemmed |
Extracting accurate time domain features from vibration signals for reliable classification of bearing faults |
title_sort |
extracting accurate time domain features from vibration signals for reliable classification of bearing faults |
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Institute of Advanced Engineering and Science |
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2018 |
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http://eprints.utm.my/id/eprint/85641/ http://dx.doi.org/10.21833/ijaas.2018.01.021 |
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