Chemometrics and Intelligent Laboratory Systems | 2021
Two-stage time-varying hidden conditional random fields with variable selection for process operating mode diagnosis
Abstract
Abstract In industrial processes, the availability of a large amount of process variables provides flexibility for process monitoring; however, too many process variables with possible redundant information can also contribute to high false positives. In order to make good use of the relevant information included in the process variables, a novel two-stage hidden conditional random field (HCRF) algorithm is developed in this paper to perform real-time process operating mode diagnosis. In the first-stage HCRF, the max-margin training strategy is employed to discriminate multiple operating modes, and by recursively eliminating the fault-irrelevant variables, the most relevant variables can be selected during the first-stage training process. On the basis of the first-stage HCRF outputs, the second-stage HCRF is proposed to adapt the dynamic changes of the process with time-varying model structure. Therefore, switchings among process operating modes can be captured to make timely diagnosis. To demonstrate the performance of the proposed algorithm, two case studies are conducted with comparisons to the conventional algorithms. Superior performance is observed through the examples.