OS1305 隠れマルコフモデルを用いた離散時系列情報の変動検知を用いた異常診断法の検討(非破壊評価と構造モニタリング,オーガナイズドセッション)

概要

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In this paper, the method of extracting true abnormal information via statistical occurrence probability analysis using HMM is examined. In general, since a fluctuation occurs in a sensor measuring quantity in the normal state by various factors, such as a noise and environment, an incorrect diagnostics arises in the abnonriality diagnosis system which use threshold value. Therefore, abnormal detection will detect more than the actually caused abnormalities significantly. Therefore, for quick action and the maintenance, reduction of the incorrect information is desired. In this paper, the method of extracting true abnormal information from the statistics analysis of the occurrence probability of timeseries of binaiy data using HMM is examined. Finally, this method succeeded in reducing 95% of incorrect information. of Mechanical Engineers.
2009-07-24