Fault detection of contactor using acoustic monitoring

Katsuhito Inoue, Edward Stewart, Mani Entezami

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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.
Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalAdvances in Mechanical Engineering
Issue number12
Publication statusPublished - 16 Dec 2021


  • Acoustic sensors
  • Condition monitoring
  • Fault detection
  • Machine learning
  • acoustic monitoring
  • audio sensor


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