Intelligent fault classification of a tractor starter motor using vibration monitoring and adaptive neuro-fuzzy inference system

Abstract

This paper presents an intelligent method for fault diagnosis of the starter motor of an agricultural tractor, based on vibration signals and an Adaptive Neuro-Fuzzy Inference System (ANFIS). The starter motor conditions to be considered were healthy, crack in rotor body, unbalancing in driven shaft and wear in bearing. Thirty-three statistical parameters of vibration signals in the time and frequency domains were selected as a feature source for fault diagnosis. A data mining filtering method was performed in order to extract the superior features among the primary thirty-three features for the classification process and to reduce the dimension of features. In this study, six superior features were fed into an adaptive neuro-fuzzy inference system as input vectors. Performance of the system was validated by applying the testing data set to the trained ANFIS model. According to the result, total classification accuracy was 86.67%. This shows that the system has great potential to serve as an intelligent fault diagnosis system in real applications.