Applications of Unsupervised Approaches for Refrigeration Showcase Data Analysis

カテゴリ: 全国大会

論文No: 3-066

グループ名: 【全国大会】平成28年電気学会全国大会論文集

発行日: 2016/03/05

タイトル(英語): Applications of Unsupervised Approaches for Refrigeration Showcase Data Analysis

著者名: Adamo Santana(Federal University of Para),福山 良和(明治大学),村上 賢哉(富士電機),松井 哲郎(富士電機)

著者名(英語): Adamo Santana(Federal University of Para),Yoshikazu Fukuyama(Meiji University),Kenya Murakami(Fuji Electric),Tetsuro Matsui(Fuji Electric)

キーワード: ショーケース,クラスタリング,教師なし学習

要約(日本語): Refrigeration showcase is utilized in super markets and convenience stores to keep various foods and drinks cool. The system, however, is susceptible to unusual events such as leakage of refrigerant coolant and frost formation, which can lead to product spoilage inside the showcases; therefore, symptoms of the unusual conditions must be identified as quickly as possible. This paper investigates possibility to apply classification methods for “showcase fault classification, assessing automatic identification by unsupervised learning.

原稿種別: 英語

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