Name
Title | ||
|---|---|---|
Structure Dictionary Learning-Based Multimode Process Monitoring and its Application to Aluminum Electrolysis Process |
Abstract | ||
|---|---|---|
Most industrial systems frequently switch their operation modes due to various factors, such as the changing of raw materials, static parameter setpoints, and market demands. To guarantee stable and reliable operation of complex industrial processes under different operation modes, the monitoring strategy has to adapt different operation modes. In addition, different operation modes usually have some common patterns. To address these needs, this article proposes a structure dictionary learning-based method for multimode process monitoring. In order to validate the proposed approach, extensive experiments were conducted on a numerical simulation case, a continuous stirred tank heater (CSTH) process, and an industrial aluminum electrolysis process, in comparison with several state-of-the-art methods. The results show that the proposed method performs better than other conventional methods. Compared with conventional methods, the proposed approach overcomes the assumption that each operation mode of industrial processes should be modeled separately. Therefore, it can effectively detect faulty states. It is worth to mention that the proposed method can not only detect the faulty of the data but also classify the modes of normal data to obtain the operation conditions so as to adopt an appropriate control strategy.
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</italic>
—Motivated by the fact that the industrial process often has different modes and they may have common patterns, this article proposes a structure dictionary learning method for multimode process monitoring. First, the structure dictionary learning method was proposed to extract the common pattern and mode-specific pattern of each mode. After two different patterns are extracted, the control limit for process monitoring can be obtained from the training data. When new data arrive, the monitoring process can be carried out. Intensive experimental results show that the proposed method performs better than other conventional methods. Compared to conventional methods, the proposed approach overcomes the assumption that each operation mode of the industrial process should be modeled separately. Therefore, it can effectively detect faulty states. Above all, it is suitable for monitoring of real industrial systems. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TASE.2020.2984334 | IEEE Transactions on Automation Science and Engineering |
Keywords | DocType | Volume |
Monitoring,Machine learning,Dictionaries,Feature extraction,Hidden Markov models,Data models,Task analysis | Journal | 17 |
Issue | ISSN | Citations |
4 | 1545-5955 | 1 |
PageRank | References | Authors |
0.36 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keke Huang | 1 | 41 | 10.22 |
| Yiming Wu | 2 | 8 | 0.82 |
| Chunhua Yang | 3 | 435 | 71.63 |
| Gongzhuang Peng | 4 | 4 | 1.83 |
| Weiming Shen | 5 | 3407 | 343.73 |