測定値時系列データからの情報抽出にもとづくビル空調システム異常状態検知

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: 2015/06/01

タイトル(英語): Fault Detection based on Information Extraction from Measured Time-series Data in Building Air-conditioning System

著者名: 湯本　真樹（近畿大学）

著者名(英語): Masaki Yumoto (Kinki University)

キーワード: ビル空調システム，異常状態検知，測定値時系列データ，専門家知識，高速フーリエ変換，判別分析　　Building air-conditioning system，Fault detection，Measured time-series data，Expert knowledge，Fast Fourier Transform，Discriminant Analysis

要約(英語): In a large scale system like building air-conditioning system, measured time-series data is observed from many kinds of sensors. It is difficult to detect the fault by the administrators because only the limited experts can diagnose the unusual system. For this reasons, a new method is required, which can detect the fault from the measured data using a computer automatically.This paper proposes the method of fault detection based on information extraction from measured time-series data in a building air-conditioning system. Fault in building air conditioning system make data generate condition “hunching”, which consists of repetition of rises and descents. The proposal method converts target measured time-series data into frequency components in order to extract condition “hunching”, and detect fault by Discriminant Analysis. Through practical experiments, it is confirmed that the proposal method can detect all faults as well as fault diagnosis using expert knowledge in a building air-conditioning system.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.135 No.6 （2015） 特集：データからの知識発見とその応用

本誌掲載ページ: 651-659 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/135/6/135_651/_article/-char/ja/