決定木を利用したルール設定支援機能を備えた設備監視システム

概要

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Proactive maintenance is important to keep normal operation of plant and equipment systems. An equipment monitoring system for such systems was developed that includes following functions: anomaly detection based on threshold method which detects anomalies by comparing a sensor signal with a threshold, anomaly detection based on data-mining which detects anomalies using statistical analysis and rule setting support for the threshold method using the result of the data-mining. The proposed method of rule setting support sets rules for the threshold method in the following procedures. First, training data is generated based on the result of data-mining. Next, a decision tree is generated by learning the training data, and an if-then rule is extracted. Usefulness of the proposed method was evaluated using 4 data sets obtained from real systems. A simple and understandable rule was extracted from a data set. The extracted rule can detect anomalies properly from the other data sets including the same fault. The rule also can explain the reason why the anomalies were detected by data-mining.