Name
Title | ||
|---|---|---|
Distributed Dynamic Process Monitoring Based on Minimal Redundancy Maximal Relevance Variable Selection and Bayesian Inference |
Abstract | ||
|---|---|---|
The conventional distributed modeling strategy generally includes all the process variables in large-scale process monitoring, thus submerging the local fault information. Meanwhile, fault diagnosis issues in the aforementioned process are also worth studying. To make up the deficiencies of the general distributed method, this brief presents an advantageously distributed fault monitoring and diagnosis scheme for large-scale dynamic processes. First, the optimal variable subblock is established by the minimal redundancy maximal relevance (mRMR) algorithm for each measured variable individually. Second, the corresponding monitoring model is set up in every subblock, respectively. Finally, all the distributed multiblocks' results are combined to form an integrated decision through the Bayesian inference. The proposed scheme considers not only the interpretation of the correlation of variables but also the redundancy between them in the block division, which better characterizes the dynamic relationships among variables, thereby facilitating the monitoring performance significantly. After that, a new contribution plot method based on subblock analysis is applied for fault isolation. Both a real-world industrial process and the Tennessee Eastman benchmark process validate that the novel monitoring strategy is more efficient than the existing state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCST.2019.2932682 | IEEE Transactions on Control Systems Technology |
Keywords | DocType | Volume |
Distributed modeling,dynamic process monitoring,minimal redundancy maximal relevance (mRMR),variable selection | Journal | 28 |
Issue | ISSN | Citations |
5 | 1063-6536 | 3 |
PageRank | References | Authors |
0.38 | 2 | 5 |