Process Systems Engineering. Feature Extraction from Time-Series Data for Process Monitoring.

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  • Fujiwara Takeshi
    Graduate School of Information Science, Nara Institute of Science and Technology Department of Environmental Engineering, Graduate School of Engineering, Kyoto University
  • Nishitani Hirokazu
    Graduate School of Information Science, Nara Institute of Science and Technology

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  • プロセスシステム工学　プロセス監視のための時系列データからの特徴抽出
  • プロセス カンシ ノ タメ ノ ジケイレツ データ カラ ノ トクチョウ チュ

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Abstract

A plant operator monitors time-series data of process variables to judge the state of process, diagnose abnormal states, and to identify failure origins. In this study, a new feature extraction method which extracts simultaneously both local features such as spikes and step changes, and the trend which characterizes global changes is provided from the viewpoint of process monitoring. In this method, the continuous function interpolated from the time-series data is represented by a series of inflection points first. Each time interval between two inflection points is called an episode. Then an approximation function of the time-series data is made iteratively by way of merging these episodes. This feature extraction method is also useful for compaction of a large number of process data.

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