Automated Bearing Fault Diagnosis using Packet Features of Vibration Signal and Gaussian Support Vector Machine
Rajeev Kumar Chauhan1, Dipti Saxena2, Jai Prakash Pandey3
1Rajeev Kumar Chauhan, Department of Electrical & Electronics Engineering, IMS Engineering College, Ghaziabad (U.P) India.
2Dipti Saxena, Department of Electrical Engineering, MNIT, Jaipur (Rajasthan) India.
3Jai Prakash Pandey, Department of Electrical Engineering, KNIT, Sultanpur (U.P) India.
Manuscript received on 23 February 2020 | Revised Manuscript received on 06 March 2020 | Manuscript Published on 18 March 2020 | PP: 72-81 | Volume-8 Issue-6S March 2020 | Retrieval Number: F10140386S20/2020©BEIESP | DOI: 10.35940/ijrte.F1014.0386S20
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Effective detection of the bearing fault and, specifically performance dilapidation assessment of a bearing is the topic of intensive analysis that may scale back prices and therefore the nonscheduled down time. This article presents an adaptive approach that is based on Bhattacharya space ranking method and dimensional reduction method as general discriminate analysis (GDA) with Gaussian support vector machine (GSVM) to accurately detect the defects of rolling bearing. For this investigation, first, vibration signal generated by rolling bearing was disintegrated to five levels employing wavelet packet (WP) method. Sixty three logarithmic wavelet packet features (LWPFs) were taken out from five level disintegrated vibration signals. After this, sixty three features were ranked by Bhattacharya space and top ten LWPFs were chosen. The top ten features were reduced to a new feature using GDA for effective detection and then applied to GSVM for detection of bearing fault. The experimental results show that new automated diagnosing approach attained classifier performance parameters as sensitivity (SE) or true positive rate, specificity (SP) or true negative rate, accuracy (AC) and positive prediction value (PPV) of 100, 98.50, 100 and 99.67 % for inner raceway (IR) and, AC: 99.49, SE: 100, SP: 98.78 and PPV: 99.87 for ball bearing (BB) at 0.18 mm diameter faults.
Keywords: Bhattacharya Space Ranking Method, Ball Bearing (BB) Defect, Gaussian Support Vector Machine, General Discriminate Analysis, Inner Race (IR), Wavelet Packet.
Scope of the Article: Machine Learning