التفاصيل البيبلوغرافية
| العنوان: |
Distributed Robust Dictionary Pair Learning and Its Application to Aluminum Electrolysis Industrial Process |
| المؤلفون: |
Jingkun Wang, Xiaofang Chen, Ziqing Deng, Hongliang Zhang, Jing Zeng |
| المصدر: |
Processes; Volume 10; Issue 9; Pages: 1850 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2022 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
high-dimension, distributed robust dictionary pair learning, process monitoring, aluminum electrolysis |
| جغرافية الموضوع: |
agris |
| الوصف: |
In modern industrial systems, high-dimensional process data provide rich information for process monitoring. To make full use of local information of industrial process, a distributed robust dictionary pair learning (DRDPL) is proposed for refined process monitoring. Firstly, the global system is divided into several sub-blocks based on the reliable prior knowledge of industrial processes, which achieves dimensionality reduction and reduces process complexity. Secondly, a robust dictionary pair learning (RDPL) method is developed to build a local monitoring model for each sub-block. The sparse constraint with l2,1 norm is added to the analytical dictionary, and a low rank constraint is applied to the synthetical dictionary, so as to obtain robust dictionary pairs. Then, Bayesian inference method is introduced to fuse local monitoring information to global anomaly detection, and the block contribution index and variable contribution index are used to realize anomaly isolation. Finally, the effectiveness of the proposed method is verified by a numerical simulation experiment and Tennessee Eastman benchmark tests, and the proposed method is then successfully applied to a real-world aluminum electrolysis process. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
Process Control and Supervision; https://dx.doi.org/10.3390/pr10091850 |
| DOI: |
10.3390/pr10091850 |
| الاتاحة: |
https://doi.org/10.3390/pr10091850 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.DC0133FC |
| قاعدة البيانات: |
BASE |