Cost-Effective Non-Invasive Vibration Measurement Using UWB Radar and RDWT for Outer and Inner Race Fault Diagnosis in BLDC Motors

Detecting electrical and mechanical faults in brushless direct current (BLDC) motors is essential for ensuring the reliability and performance of electric vehicles. Vibration monitoring plays a key role in fault identification and can be implemented through invasive or non-invasive methods. However, existing techniques often involve high costs, complex hardware, or limited sensitivity. This study proposes a low-cost, non-invasive vibration measurement approach that integrates a handheld ultra-wideband (UWB) radar, a data recording unit, and advanced signal processing for diagnosing outer- and inner-race bearing faults. In this method, the UWB radar generates a high-frequency signal projected onto the motor, and the reflected response is recorded via SIGVIEW software. The captured data are processed using a software phase-locked loop (SPLL) with low-pass filtering, followed by Rational Dilation Wavelet Transform (RDWT) analysis. Energy distributions of RDWT sub-bands, evaluated in MATLAB R2022b, are compared under normal and faulty conditions. Results demonstrate that as bearing faults increase, RDWT sub-band energy at level-4 rises by 4.57%–5.15%, while level-5 shows an increase of 6.27%–16.56%. The proposed method enables early fault detection in BLDC motors, supporting predictive maintenance and minimizing unexpected failures in electric vehicles.