Evaluation of 2D Acoustic Signal Representations for Acoustic-Based Machine Condition Monitoring

Gbanaibolou Jombo, Ajay Shriram

Research output: Contribution to conferencePaperpeer-review

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Abstract

Acoustic-based machine condition monitoring (MCM) provides an improved alternative to conventional MCM approaches, including vibration analysis and lubrication monitoring, among others. Several challenges arise in anomalous machine operating sound classification, as it requires effective 2D acoustic signal representation. This paper explores this question. A baseline convolutional neural network (CNN) is implemented and trained with rolling element bearing acoustic fault data. Three representations are considered, such as log-spectrogram, short-time Fourier transform and log-Mel spectrogram. The results establish log-Mel spectrogram and log-spectrogram, as promising candidates for further exploration.
Original languageEnglish
Pages1-2
Number of pages2
Publication statusPublished - 12 Apr 2022
EventPECS 2022 Physics, Engineering and Computer Science Research conference, University of Hertfordshire - University of Hertfordshire, School of Physics, Engineering and Computer Science (online), Hatfield, United Kingdom
Duration: 12 Apr 202212 Apr 2022

Conference

ConferencePECS 2022 Physics, Engineering and Computer Science Research conference, University of Hertfordshire
Country/TerritoryUnited Kingdom
CityHatfield
Period12/04/2212/04/22

Keywords

  • Machine Condition Monitoring
  • Detection and Classification of Anomalous Machine Operating Sound
  • Industrial Sound Analysis
  • Machine Hearing

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