Anomaly detection from online monitoring of system operations using recurrent neural network

Taiki Kubota, Watalu Yamamoto

研究成果: Conference article査読

8 被引用数 (Scopus)

抄録

In production systems, the operation data is multivariate time series data including internal state of the system, control variables, control parameters and the like. As monitoring centre collects data intensively, monitoring time differs for each system. The predetermined frequency of data recording per day may not be protected. In this study, we decided to use a RNN which can learn data with missing values. The neural network learns diagnosis of system abnormality from the operation data of the system and the data of the maintenance record. Then we examine the usefulness of prediction of abnormal occurrence of learned neural network.

本文言語English
ページ(範囲)83-89
ページ数7
ジャーナルProcedia Manufacturing
30
DOI
出版ステータスPublished - 2019
外部発表はい
イベント14th Global Congress on Manufacturing and Management, GCMM 2018 - Brisbane, Australia
継続期間: 2018 12月 52018 12月 7

ASJC Scopus subject areas

  • 産業および生産工学
  • 人工知能

フィンガープリント

「Anomaly detection from online monitoring of system operations using recurrent neural network」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