Fault detection and diagnosis with random forest feature extraction and variable importance methods
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The ever-present drive to safer, more cost-effective and cleaner processes motivates the exploration of a variety of process monitoring methods. In the domain of data-driven approaches, random forest models present a nonlinear framework. Random forest models consist of ensembles of classification and regression trees in which the model response is determined by voting committees of independent binary decision trees. Data-driven approaches to fault diagnosis often involve summarizing potentially large numbers of process variables in lower dimensional diagnostic sequences. Random forest feature extraction allows for the monitoring of process in feature and residual spaces, while random forest variable importance measures can potentially be used to identify process variables contribution to fault conditions. In this study, a framework for diagnosing steady state faults with random forests is proposed and demonstrated with a simple nonlinear system and the benchmark Tennessee Eastman process.
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