216 Time and Cause-Effect Data Evaluation and Prediction for Solving Fault Diagnosis and Maintenance Problems

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概要

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• The concept of the Mutually Excluding Relations is presented in the paper in order to evaluate the system performance and to discover any abnormality in its operation. For such purpose a cause-effect relation dynamic models are used for revealing the existing relationships between the pairs of parameters in the preliminary designed observation set of parameters. Two Identification schemes are presented, namely for off-line and for real-time identification, based on the Least Squares Estimation and on the Widrow-Hoff learning rule. They are used to create the normal operation model and the current operation model of the system. The degree of discrepancy between these models during the operation of the system is used as a measure for detecting the abnormality of its operation. Computer simulations have been performed that demonstrate in a pictorial way the main idea and its applicability for real-time monitoring, diagnosis and maintenance.

• 一般社団法人日本機械学会の論文
• 2002-12-03

著者

• Komatsu Koji

  香川大

• VACHKOV Gancho

  香川大

• KIYOTA Yuhiko

  香川大

関連論文

• 216 Time and Cause-Effect Data Evaluation and Prediction for Solving Fault Diagnosis and Maintenance Problems

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