Bearings Fault Diagnosis in Electromechanical System Using Transient Motor Current Signature Analysis

The bearing faults may lead to expensive and catastrophic failures, which affect the reliability of power drivetrain in electromechanical system. To reduce impact of the bearing failures, accurate analysis and timely diagnosis are required for improved reliability of the electromechanical systems. In this work, an enhanced transient current signature analysis has been investigated for aircraft application using permanent magnet synchronous motor with bearing defects. The motor current under steady-state and transient conditions is acquired from an experimental test-rig with bearing defects at different loading and speed levels. The acquired signals are first investigated using frequency domain analysis and then compared with the time–frequency domain analysis such as wavelet analysis. The discrete wavelet transform is used to analyze on the calculated residual current for the bearing fault diagnosis. The proposed time–frequency-based technique is able to provide useful features that characterize the condition of the bearings on transient current. Further, back-propagation neural network is used over the calculated features which distinguishes the defective bearing from the healthy bearing with high accuracy.