Bearing fault diagnosis based on wavelet transform and fuzzy inference

Abstract

This paper deals with a new scheme for the diagnosis of localised defects in ball bearings based on the wavelet transform and neuro-fuzzy classification. Vibration signals for normal bearings, bearings with inner race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the accelerometer signals and to generate feature vectors. An adaptive neural-fuzzy inference system (ANFIS) was trained and used as a diagnostic classifier. For comparison purposes, the Euclidean vector distance method as well as the vector correlation coefficient method were also investigated. The results demonstrate that the developed diagnostic method can reliably separate different fault conditions under the presence of load variations.