Publications

Abstract : Objectives: Being widely used in most of the industrial machineries, bearings are subjected to wear and tear. Failure of bearings can incur heavy losses in the industries. In order to prevent such mishaps during operation, it is necessary to subject the bearings to a suitable fault diagnosis technique. Methods/Statistical Analysis: Vibration analysis is performed to detect the fault in bearings. For the fault analysis, vibration signals were taken for good, inner race defect, outer race defect and combination of these defects. Since vibration signals are complex and the defect related signature is buried deep within the noise and high frequency resonance, simple signal processing cannot be used for effectively detecting bearing fault. In this paper, discrete wavelets transform were used to detect bearing faults. For wavelet and feature selection, J48 decision tree algorithm was used. For feature classification, Best First Tree (BFT) algorithm was used. Findings: The experimental results indicate biorthogonal wavelets show maximum successful bearing fault detection rate. The classification accuracy was calculated and found to be 96.25%. This result is further refined to get better classification accuracy and the final result was found to be 98%. Application/Improvements: This can be considered to be a part of a preventive maintenance method in order to avoid mishaps in industries. The classification accuracy can be further improved using different algorithms.