Efficient GAN-Based Unsupervised Anomaly Sound Detection for Refrigeration Units

Shouichi Hatanaka, Hiroaki Nishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A smart factory or Industry 4.0 is creating an epoch for manufacturing and its production lines. It reduces the total cost by monitoring and predicting the expected faults of factory lines and products. One of the essential challenges is to develop a technology to detect and predict abnormalities at an early stage without human resources. For this reason, the automation of anomaly detection is now attracting attention. Many statistical and machine-learning methods have been studied for anomaly detection. In this study, we focus on a refrigeration system for large storage, where the failures of the system will cause enormous losses. Moreover, this type of system was independently designed according to the environment, location, and storage items. Under this condition, it is difficult to train discriminative models for anomaly detection using training data that include failure data. In addition, it is indispensable to provide a basis for determining whether the system is abnormal to achieve future treatments. Therefore, deep generative models are used to achieve unsupervised abnormality detection. Because the sensing system's cost for detecting system failures should be reduced, the proposed system uses low-cost microphone arrays to monitor sounds and source locations. The system also provides a rationale by visualizing and mentioning irregular sounds. Furthermore, this study compared various deep generative models in terms of accuracy and showed that the Efficient GAN-based method achieved the highest accuracy.

Original languageEnglish
Title of host publicationProceedings of 2021 IEEE 30th International Symposium on Industrial Electronics, ISIE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728190235
DOIs
Publication statusPublished - 2021 Jun 20
Event30th IEEE International Symposium on Industrial Electronics, ISIE 2021 - Kyoto, Japan
Duration: 2021 Jun 202021 Jun 23

Publication series

NameIEEE International Symposium on Industrial Electronics
Volume2021-June

Conference

Conference30th IEEE International Symposium on Industrial Electronics, ISIE 2021
Country/TerritoryJapan
CityKyoto
Period21/6/2021/6/23

Keywords

  • anomaly detection
  • deep generative model
  • deep learning
  • Efficient GAN
  • GAN
  • refrigeration units

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering

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