Automated Bearing Fault Diagnosis using Packet Features of Vibration Signal and Gaussian Support Vector Machine

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.