ランダムフォレストを用いたベルトコンベヤ火災検知

カテゴリ: 論文誌(論文単位)

グループ名: 【A】基礎・材料・共通部門

発行日: 2021/09/01

タイトル(英語): Fire Detection of Belt Conveyor Using Random Forest

著者名: 古川　靖（横河電機（株）IA-PS事業本部ITセンター）

著者名(英語): Osamu Furukawa (IT Center, IA-PS Headquarters, Yokogawa Electric Corporation)

キーワード: ランダムフォレスト，ベルトコンベヤ，火災検知，ラマン散乱，DTS，ROTDR　　random forest，belt conveyor，fire detection，Raman scattering，DTS，ROTDR

要約(英語): This study investigates a fire detection method using random forest that is one of the machine learning algorithms, which is applied to catch signs from minute temperature changes indirectly measured by a temperature sensor deployed on an idler of a belt conveyor. Belt conveyors are often used outdoors and the ambient temperature changes, which makes it difficult to distinguish them from temperature changes due to fire, especially for a conventional simple threshold determination. Based on features of measured temperature with a trend, random forests classify whether the individual temperature is abnormal or not. It is described that even if the individual classification results include false detections, false classification can be reduced by rearranging the individual results in temporal order. To implement this temporal factor, a method of using the past classification result as a feature quantity is proposed. Simulations are conducted with the feature quantities added the preceding classification result, and it is shown that false detections are eliminated.

本誌: 電気学会論文誌A（基礎・材料・共通部門誌） Vol.141 No.9 （2021） 特集：イノベーションを創出する最新の計測技術2021

本誌掲載ページ: 508-513 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/ieejfms/141/9/141_508/_article/-char/ja/