Maximal Information Coefficientを用いた変数選択手法に基づくKernel PCAベースMSPCによるショーケースシステムの異常検知

カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門

発行日: 2021/04/01

タイトル(英語): Unsupervised Fault Detection for Refrigeration Showcase Systems with Kernel Principal Component Analysis based Multivariate Statistical Process Control using Feature Selection with Maximal Information Coefficient

著者名: 新井　馨（明治大学大学院 先端数理科学研究科 ネットワークデザイン専攻），福山　良和（明治大学大学院 先端数理科学研究科 ネットワークデザイン専攻），村上　賢哉（富士電機（株）技術開発本部 デジタルイノベーション研究所 AIソリューションセンター），松井　哲郎（富士電機（株）技術開発本部 デジタルイノベーション研究所 AIソリューションセンター）

著者名(英語): Kiyo Arai (Graduate School of Advanced Mathematical Sciences, Meiji University), Yoshikazu Fukuyama (Graduate School of Advanced Mathematical Sciences, Meiji University), Kenya Murakami (AI Solution Center, Digital Innovation Laboratory, Corporate R&D Hea

キーワード: ショーケースシステム，異常検知，変数選択，カーネル主成分分析，多変量統計的プロセス管理，maximal information coefficient　　showcase system，fault detection，feature selection，kernel principal component analysis，multivariate statistical process control，maximal information coefficient

要約(英語): This paper proposes a kernel principal component analysis (KPCA) based multivariate statistical process control (KPCA-MSPC) method for fault detection of refrigeration showcase systems using a feature selection method with maximal information coefficient (MIC). Refrigeration showcase system data include non-linear relationships among pairs of features, and only normal data can be available for training generally. KPCA-MSPC is suitable for the fault detection because it is an unsupervised method and can handle non-linear relationships. In showcase systems, a large number of measured data can be obtained and they can be utilized as features for fault detection. However, considering system costs, the number of sensors installed in the showcase systems and the amount of data stored in data centers are limited. Therefore, a feature selection method based on MIC and k-nearest neighbor algorithm (KNN) (MIC-KNN-FS) suitable for KPCA-MSPC is proposed. The effectiveness of the combination of KPCA-MSPC and the proposed MIC-KNN-FS for showcase systems is verified by comparison with the Laplacian Score feature selection method (LS-FS) and the KNN feature selection method (KNN-FS), which are typically utilized as feature selection methods, and cumulative autoencoders (CAE) and MSPC based on PCA (PCA-MSPC), which are unsupervised fault detection methods.

本誌: 電気学会論文誌D（産業応用部門誌） Vol.141 No.4 （2021） 特集：半導体電力変換研究会

本誌掲載ページ: 345-353 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejias/141/4/141_345/_article/-char/ja/