Continuous Acoustic Monitoring of Electrical Machines; Processing Signals from USB Microphone Mobile Smartphone Sensors Detecting DC Motor Controller Fault

摘要

Transient current instability is one of the most common faults evident in Pulse Width Modulation (PWM) controlled brushless DC motors. This paper explores the under-developed field of real-time acoustic diagnostics for electrically based faults using consumer grade sensors. Current instabilities produce an audible torque transient on the motor, easily detectable using consumer acoustic sensors; a USB microphone and smartphone in this case. Two time-frequency signal processing techniques, Wavelet Packet Transform (WPT) and Empirical Mode Decomposition (EMD), are used to isolate information pertaining to the fault and are assessed for computational performance. This gives four processed signals to search for instabilities using a peak finding technique. We then compare the performance of each method. With the USB microphone WPT signal correlating the best results (93%), a simplistic logarithmic predictive model is used to estimate the durations for the next experimental run, in real-time. The results prove that readily accessible and affordable consumer acoustic sensors can be used for real-time fault diagnostics with a high degree of accuracy.