Visual explanation of neural network based rotation machinery anomaly detection system

Mao Saeki, Jun Ogata, Masahiro Murakawa, Tetsuji Ogawa

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781538683576
DOI
出版ステータスPublished - 2019 6
イベント2019 IEEE International Conference on Prognostics and Health Management, ICPHM 2019 - San Francisco, United States
継続期間: 2019 6 172019 6 20

出版物シリーズ

名前2019 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

ASJC Scopus subject areas

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

フィンガープリント 「Visual explanation of neural network based rotation machinery anomaly detection system」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル