Name
Abstract | ||
|---|---|---|
Process monitoring is a common strategy for monitoring the industrial production operation state. It can detect abnormal conditions in industrial processes and provide certain guidance for production. Many classical data-driven process monitoring approaches neglect the non-Gaussian and nonlinearity of the data. For solving the above problems, this paper designs an overcomplete broad learning system (OBLS) with incremental learning ability. The method combines multiple fault data into one data matrix. Then the overcomplete approach is employed to capture the non-Gaussian information from the original data to obtain a mixed matrix. Next, the weights of the OBLS network are trained according to the extracted feature matrix containing non-Gaussian information and its corresponding fault label. Meanwhile, the nonlinearity of the data is addressed by the OBLS network. Finally, the incremental learning capabilities of the OBLS network enable it to be updated quickly when new fault samples are added to the training set without entire retraining process. The experimental results in numerical examples, penicillin fermentation simulation platform and real-world industrial process demonstrate the superiority and feasibility of the OBLS model. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.engappai.2020.104139 | Engineering Applications of Artificial Intelligence |
Keywords | DocType | Volume |
Process monitoring,Batch processes,Overcomplete independent component analysis broad learning system,Incremental learning | Journal | 99 |
ISSN | Citations | PageRank |
0952-1976 | 1 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Chang | 1 | 2 | 2.05 |
| Ruiwei Lu | 2 | 1 | 0.34 |