Control of mechatronics systems: Ball bearing fault diagnosis using machine learning techniques

Ball bearing fault is one of the main causes of induction motor failure. This paper investigates in the fault diagnosis of ball bearing of three phase induction motor using random forest algorithm and C4.5 decision tree. The bearing conditions are classified to four categories: normal, bearing with inner race fault, bearing with ball fault and bearing with outer race fault. The statistical features used for classification are extracted from mechanical vibration signal in time domain and frequency domain. Principal component analysis (PCA) and linear discriminent analysis (LDA) are used to reduce the dimension and complexity of the feature set. The classification accuracy of random forest algorithm and C4.5 decision tree are analyzed and compared. The experimental results show that the random forest algorithm not only works better than the C4.5 decision tree but also can classify the ball bearing condition effectively.