Fault diagnosis using dynamic trend analysis: A review and recent developments

Dynamic trend analysis is an important technique for fault detection and diagnosis. Trend analysis involves hierarchical representation of signal trends, extraction of the trends, and their comparison (estimation of similarity) to infer the state of the process. In this paper, an overview of some of the existing methods for trend extraction and similarity estimation is presented. A novel interval-halving method for trend extraction and a fuzzy-matching-based method for similarity estimation and inferencing are also presented. The effectiveness of the interval halving and trend matching is shown through simulation studies on the fault diagnosis of the Tennessee Eastman process. Industrial experiences on the application of trend analysis technique for fault detection and diagnosis is also presented followed by a discussion on outstanding issues and solution approaches.

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