Name
Title | ||
|---|---|---|
Recursive Hybrid Variable Monitoring for Fault Detection in Nonstationary Industrial Processes |
Abstract | ||
|---|---|---|
Practical industrial processes usually have nonstationary properties, which make the monitoring more challenging because the fault information may be buried by nonstationary trends. For nonstationary processes, many methods have been proposed for fault detection based on continuous variables. However, binary variables may appear together with continuous variables in modern industrial processes. To address the issue of process monitoring with hybrid variables and nonstationarity, a model named recursive hybrid variable monitoring (RHVM) is proposed in this paper. For RHVM, recursive strategy is utilized to suppress nonstationary trend and to reveal fault information. In addition, RHVM has the ability of model self-updating with arriving samples. The closed-form updates of required parameters are derived in detail and the improvement of performance is analyzed. At last, the superiority of the proposed model is demonstrated by a simulation example and a practical nonstationary process of a power plant. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TII.2022.3151072 | IEEE Transactions on Industrial Informatics |
Keywords | DocType | Volume |
Fault detection,hybrid variable,nonstationary process,process monitoring,recursive hybrid variable monitoring (RHVM) | Journal | 18 |
Issue | ISSN | Citations |
10 | 1551-3203 | 0 |
PageRank | References | Authors |
0.34 | 21 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Wang | 1 | 76 | 27.77 |
| Dong-Hua Zhou | 2 | 1833 | 129.73 |
| Maoyin Chen | 3 | 241 | 28.51 |