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Abstract

In case-based anomaly detection in equipment such as a plant, it is necessary to search entire learned data for partial data close to newly observed data. Which needs a long computation time. In order to solve this problem, there is provided a method, the learned data is clustered into clusters and the centers of the clusters as well as data pertaining to the clusters are stored in advance. Data close to newly observed data is selected from only the learned data pertaining to a cluster close to the newly observed data. Then, a normal model is created from the selected data and an anomaly measure is found whereas a threshold value is determined. Subsequently, an anomaly measure is found from the newly observed data and the created normal model. Then, this anomaly measure is compared with the threshold value in order to detect an anomaly of the equipment.

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