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A Comprehensive Study of Fault Diagnostics of Roller Bearings Using Continuous Wavelet Transform

V Sugumaran, Aditya Vasudev Rao, K. I. Ramachandran


One of the prominent causes of breakdown of rotating machinery, bearing failure, proves to be costly to the industry. Unexpected machine stoppage disrupts production schedule and maintenance strategies. Therefore, condition monitoring has become a very important research field in recent years. It can be used to avoid unexpected failures of critical systems. Bearing fault diagnostics using vibration signals has proven to be very effective. This paper deals with the fault detection and classification of roller bearings using Continuous Wavelet Transform. This paper is the result of an exhaustive study of different families wavelets and the determination of the best wavelet for the purpose. A decision tree is constructed using the C4.5 algorithm to determine the best wavelet based on classification accuracy. Once the wavelet was selected, those features were used in two classifiers viz. Support Vector Machines (SVM) and Proximal Support Vector Machines (PSVM) to determine classification accuracy. The test results showed that 'rbio1.5' gives maximum classification accuracy among wavelets and PSVM gave 100% classification accuracy for all of the bearing faults thus proving to be most suitable for practical applications

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