Unsupervised Fault Detection in Refrigeration Showcase with Single Class Data using Autoencoders

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2019/10/01

タイトル(英語): Unsupervised Fault Detection in Refrigeration Showcase with Single Class Data using Autoencoders

著者名: Adamo Santana (Fuji Electric, Co. Ltd.), Yu Kawamura (Fuji Electric, Co. Ltd.), Kenya Murakami (Fuji Electric, Co. Ltd.), Tatsuya Iizaka (Fuji Electric, Co. Ltd.), Tetsuro Matsui (Fuji Electric, Co. Ltd.), Yoshikazu Fukuyama (Meiji University)

著者名(英語): Adamo Santana (Fuji Electric, Co. Ltd.), Yu Kawamura (Fuji Electric, Co. Ltd.), Kenya Murakami (Fuji Electric, Co. Ltd.), Tatsuya Iizaka (Fuji Electric, Co. Ltd.), Tetsuro Matsui (Fuji Electric, Co. Ltd.), Yoshikazu Fukuyama (Meiji University)

キーワード: artificial neural networks，classification algorithms，fault detection，machine learning

要約(英語): Refrigeration showcases are commonly utilized equipment in super markets and convenience stores to maintain the temperature and quality of products. Being also susceptible to fault events, the detection of symptoms of unusual operation is still difficult as only samples of normal behavior are usually available. This paper introduces a new use of autoencoders for this one class classification problem with only normal data. An unsupervised approach to cumulatively flag abnormal events is proposed based on ensembled autoencoders and compared with a deep learning counterpart, one-class support vector machine, and the multivariate statistical model standardly employed for fault analysis by the showcase industry manufacturer. Results showed the robustness of the method in flagging out-of-control samples, even when trained with raw sensor data without prior preprocessing.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.139 No.10 （2019） 特集Ⅰ：インテリジェント・システム　特集Ⅱ：2018電気関係学会四国支部連合大会

本誌掲載ページ: 1191-1200 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/139/10/139_1191/_article/-char/ja/