Fault diagnosis of rolling element bearing using continuous wavelet transform and K- nearest neighbour

Abstract

Rotating machines are a common class for machineries in industries and one of the root cause of failure of these machines are faults in rolling element bearing (REB). Therefore, to prevent machinery performance degradation and malfunctioning, an effective bearing fault diagnosis technique is vital. So, the use of different machine learning techniques proposes a new method of diagnosing REB defects. Machine learning (ML) method namely K-nearest neighbour (KNN) is used to classify different types of REB faults. In this article, vibration signals are acquired from a customized test rig for three conditions of deep groove ball bearings: Normal (N), defect on Inner Race (IR) and defect on Outer Race (OR) at different loads and maximum speed. Continuous Wavelet Transform (CWT) has been applied to the acquired signal and Seventeen Time Frequency Domain (TFD) statistical features were extracted from CWT coefficients. KNN classifier is trained using these TFD. The classification accuracy on test data i.e., 83.3% reveals that KNN can be effectively used to classify different conditions of REB.