Fault detection for railway traction motor bearing through leakage current

Abstract

Much research on detecting machinery faults in the early stage is being conducted for the purpose of preventing failure and reducing maintenance effort simultaneously. In this paper, a bearing fault detection method for a railway traction motor through leakage currents is proposed. The proposed detection method combines octave band analysis and machine learning. Experiments simulating abnormalities due to defective bearings were conducted and the effectiveness of the proposed method was verified. These experiments showed that the proposed method can successfully detect failures in railway traction systems in relation to specific conditions and that leakage current could potentially be used to detect bearing faults.