Support Vector Machine with Wavelet Decomposition Method for Fault Diagnosis of Tapered Roller Bearings by Modelling Manufacturing Defects

Abstract

Tapered roller element bearings are generally applied in machines and transmission gearboxes. In manufacturing outer ring, inner ring and the rollers usually suffer damages. It is a challenging task to reveal and classify the defects. This paper presents an efficient method for fault classification by support vector machines. The faults on the bearing parts created by laser beam machine have similar shape and surface topography as the grinding faults from the manufacturing process. Vibration signature is collected by sensitive transducer and high resolution data acquisition unit. A test-rig is constructed to model the circumstances of the operation of the built-in tapered roller bearings. Moreover, test-rig is planned with the aim to mitigate the harmful vibration components from the environment that influence the precision of the vibration measurement. Feature extraction is executed by wavelet decomposition. Decomposition level is determined by FFT considering the structural frequencies of the bearing elements. The proper wavelet is selected by the Energy-to-Shannon Entropy criteria from Daubechies and Symlet wavelet families. The fault classification is done by R Cran software using support vector machine classifiers. Time domain parameters of the vibration signature such as kurtosis, skewness, crest factor and range are provided to the classifier. Classification rates are high enough to ensure the efficiency of the method in all cases in the study.