Development of EBP-Artificial neural network expert system for rolling element bearing fault diagnosis

The objective of this work is to develop techniques to automate the condition-based maintenance procedure. It is observed that vibration signals are capable of alarming the malfunctions in machineries. In order to overcome the shortcomings in the traditional vibration analysis using time-domain and frequency-domain features, two new approaches based on wavelet transform, artificial neural network and fuzzy rules are proposed for detecting and localizing defects in rolling element bearings. The two expert systems are developed and tested with the use of vibration signals collected from the bearing housing of an experimental setup. Experiment results show that the proposed approaches are sensitive and reliable in detecting defects on the outer race, inner race and rolling elements of bearings. The proposed approaches may be used for other fault diagnoses such as gear faults, coupling faults, belts in industries. It is also expected from the obtained results that the generalized defect detection will be easier in future by using the proposed approaches via other parameters such as noise, temperature, lubricant analysis in addition to used vibration signals.