Visual explanation of neural network based rotation machinery anomaly detection system

Mao Saeki, Jun Ogata, Masahiro Murakawa, Tetsuji Ogawa

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

Abstract

To make a practical anomaly detection system for rotating machinery in large infrastructures, such as wind turbines, providing an explanation along with the detection results is important so that faults can be easily verified by human experts. Therefore, a method for providing a visual explanation of the predictions of a convolutional neural network (CNN)based anomaly detection system is considered in this paper. More specifically, the CNN used takes the monitoring target machine’s vibrational data as input and predicts whether the target’s state is healthy or anomalous. A CNN visualization technique is applied this network to obtain an explanation of its predictions. In order to evaluate the obtained explanation, it is compared with an expert diagnosis made on the same data set. The results indicate that the frequency used by the experts to detect faults was also included in the network’s explanation, indicating that the proposed visualization method can be used to provide useful information to help experts verify faults.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538683576
DOIs
Publication statusPublished - 2019 Jun
Event2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019 - San Francisco, United States
Duration: 2019 Jun 172019 Jun 20

Publication series

Name2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019

Conference

Conference2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019
CountryUnited States
CitySan Francisco
Period19/6/1719/6/20

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Keywords

  • Anomaly detection
  • Condition monitoring
  • Data-driven method
  • Machine learning
  • Vibration signals
  • Visualization

ASJC Scopus subject areas

  • Aerospace Engineering
  • Mechanical Engineering
  • Safety, Risk, Reliability and Quality

Cite this

Saeki, M., Ogata, J., Murakawa, M., & Ogawa, T. (2019). Visual explanation of neural network based rotation machinery anomaly detection system. In 2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019 [8819396] (2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPHM.2019.8819396