Distributed status evaluation method based on evidence revision fusion

Abstract

BackgroundComplex process industrial process is large in scale, with numerous variables and complex variable relationship. It is difficult to construct an overall status evaluation model to accurately evaluate the operation status. Compared with holistic modeling status evaluation, distributed status evaluation can make better use of potentially valuable local details. Besides, the integration of result from multiple status evaluation models using decision fusion method such as Dempster-Shafer (DS) evidence theory can make the model more robust. However, DS evidence theory produces counterintuitive result when dealing with high conflict evidence. MethodsIn this paper, a distributed status evaluation method based on evidence revision fusion is proposed. Firstly, a partition method combining qualitative and quantitative knowledge is proposed, which improves the ability to capture local effective information. Secondly, a new status fusion evaluation method is proposed. Feature fusion technique is used to process the original process data, and the evaluation results of multiple status evaluation models are fused by the decision fusion method. Finally, a conflict management strategy with parallel supervised and unsupervised revision is proposed to revise high-conflict evidence from different status evaluation models. FindingsThe proposed method shows good performance in Tennessee-Eastman process, which demonstrates the effectiveness of the method.