Anomaly detection and cause identification based on sensor data

被引：0

作者：

Moriyama T. ^{[1
]}

Maeda S. ^{[2
]}

Member T.S. ^{[3
]}

机构：

[1] Hiroshima Institute of Technology, Graduate School of Science and Technology Electrical and Electronic Engineering, 2-1-1, Miyake, Saeki-ku, Hiroshima

[2] Hiroshima Institute of Technology, Faculty of Engineering, Department of Electronics and Computer Engineering, 2-1-1, Miyake, Saeki-ku, Hiroshima

[3] Hitachi Power Solutions Co., Ltd., 38, Shinkou-cho, Hitachinaka, Ibaraki

来源：

IEEJ Transactions on Electronics, Information and Systems | 2019年 / 139卷 / 12期

关键词：

Anomaly detection; Forecast error variance decomposition; Time series data; Vector auto regression model;

D O I：

10.1541/ieejeiss.139.1517

中图分类号：

学科分类号：

摘要：

Maintenance tasks allow us to find faults in its early stage, and extend service life of facilities. Now, facilities and systems, which maintenance tasks are done under status monitoring, tend to be increasing, but there are still a lot of facilities that are executed under time plan maintenance. In this study, anomaly detection method is proposed with forecast error variance decomposition based on data provided from each sensor assembled in facilities. © 2019 The Institute of Electrical Engineers of Japan.

引用

页码：1517 / 1526

页数：9

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