Fault detection of contactor using acoustic monitoring

Abstract

Recently, condition monitoring methods using the sound of the machine have attracted attention. Since approaching high voltage equipment increases the risk of electrocuting, non-contact data acquisition is desirable. Most of the research targets of acoustic monitoring are rotating machines and it is not clear whether it is effective for machines that switch between two states, such as contactors and circuit breakers. In this work, several investigations have been carried out on the acoustic condition monitoring of contactor. The Mel-frequency cepstrum coefficients (MFCCs) were obtained from the sound data of the contactors under normal and simulated fault conditions. Support Vector Machine (SVM) was trained with MFCCs and found that it could detect and diagnose contactor faults with high accuracy.

A Novel Methodology for Unsupervised Anomaly Detection in Industrial Electrical Systems

“…Achieving this goal, therefore, requires using advanced analytics and maximizing not only the amount of data acquired but also the number of parameters monitored. Parameters that can provide indications of possible breakdowns can be captured with infrared thermography [24], acoustic monitoring [25], vibration analysis [26], oil analysis [27], and electrical analysis [28].…”