Optimal wavelet selection for the size estimation of manufacturing defects of tapered roller bearings with vibration measurement using Shannon Entropy Criteria

Abstract

Fault diagnosis of bearings is essential in manufacturing to increase quality. Traditionally, fault diagnosis of tapered roller element bearings is performed by signal processing methods, which handle the nonstationary behaviour of the signal. The wavelet transform is an efficient tool for analysing the vibration signal of the bearings because it can detect the sudden changes and transient impulses in the signal caused by faults in the bearing elements. In this article, manufacturing faults on the outer ring of tapered roller bearings due to the grinding process in manufacturing are investigated. Nine different real values wavelets (Symlet-2, Symlet-5, Symlet-8, db02, db06, db10, db14, Meyer, and Morlet) are compared according to the Energy-to-Shannon-Entropy ratio criteria, and which is efficient for detecting the manufacturing faults is determined. Finally, experiments are carried out on a test rig for determining the geometrical size of the manufacturing faults with all wavelets directly from the vibration signature the result of db02, Symlet-5, and Morlet wavelets are presented. When modelling the bearing structure as an under-damped second-order mass-spring-damper mechanical system, its unit impulse response function is compared to the wavelets on the basis of their Energy-to-Shannon-Entropy ratio to determine the fault size from the vibration signal. The proposed technique has been successfully implemented for measuring defect widths. The maximum deviation in result has been found to be 4.12 % for the defect width which was verified with image analysis methods using an optical microscope and contact measurement.