設備異常診断用の時系列データ検索言語TPQL

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: 2014/01/01

タイトル(英語): Time Series Data Query Language TPQL for Anomaly Detection in Facility

著者名: 今村　誠（三菱電機（株）情報技術総合研究所），竹内　丈志（三菱電機（株）情報技術総合研究所），北上　眞二（三菱電機（株）情報技術総合研究所），菅野　幹人（三菱電機（株）情報技術総合研究所），撫中　達司（三菱電機（株）情報技術総合研究所）

著者名(英語): Makoto Imamura (Information Technology R&D Center, Mitsubishi Electric Corporation), Takeshi Takeuchi (Information Technology R&D Center, Mitsubishi Electric Corporation), Shinji Kitagami (Information Technology R&D Center, Mitsubishi Electric Corporation), Mikihito Kanno (Information Technology R&D Center, Mitsubishi Electric Corporation), Tatsuji Munaka (Information Technology R&D Center, Mitsubishi Electric Corporation)

キーワード: 時系列データ，ウインドウ，畳み込み和，設備診断，異常検知，時間データベース　　Time Series Data，Window，Convolution Sum，Facility Diagnosis，Anomaly Detection，Temporal Database

要約(英語): In facility management for plants and buildings, needs of facility diagnosis for saving energy or facility management cost by analyzing time series data from sensors of equipments in facilities have been increasing. In this paper, we propose a relation-based query language TPQL (Trend Pattern Query Language) for expressing constraints in time series data for anomaly detection in facilities and implemented an anomaly detection system based on TPQL. The features of TPQL are the following. (1) TPQL introduces a convolution operator into SQL (Structured Query Language) in order to describe contextual anomaly conditions over window sequences such as duration constraint and hunting constraint. (2) TPQL introduces time-interval based join into SQL in order to join time series data with different sampling rates. The anomaly detection system consists of a TPQL-interpreter as a top-level engine, relational database as an SQL engine, a key-value store database as a large data storage and configure management information to represent target signals for diagnosis and threshold values for anomaly detection. We evaluate that the system has enough expression ability to describe domain dependent anomaly detection conditions with TPQL over sliding windows and the sufficient processing speed required by the real applications.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.134 No.1 （2014） 特集：スマート社会を支える電子回路技術

本誌掲載ページ: 156-167 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/ieejeiss/134/1/134_156/_article/-char/ja/