A Data-driven Approach for Fault Detection with Uncertainty in Historical Modes
نویسندگان
چکیده
Bayesian methods are a kind of data-driven methods developed in recent years and have played an important role in fault detection and diagnosis. Nevertheless, traditional Bayesian fault detection methods cannot deal with the case where some underlying modes are ambiguous in the historical data. To cope with this problem, a new method is presented in this paper. The modes with uncertainty in historical data are classified under a Bayesian framework by combing historical data, current evidence and prior knowledge. In order to improve the detection performance, weighted kernel density estimation is employed in likelihood estimation. The proposed method is tested with Tennessee Eastman (TE) process benchmark data and shows better performance compared to previous approaches.
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