Academic Journal
Fault detection using machine learning based dynamic ICA-distributed CCA: Application to industrial chemical process
| العنوان: | Fault detection using machine learning based dynamic ICA-distributed CCA: Application to industrial chemical process |
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| المؤلفون: | Husnain Ali, Zheng Zhang, Rizwan Safdar, Muhammad Hammad Rasool, Yuan Yao, Le Yao, Furong Gao |
| المصدر: | Digital Chemical Engineering, Vol 11, Iss , Pp 100156- (2024) |
| بيانات النشر: | Elsevier, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Chemical engineering LCC:Information technology |
| مصطلحات موضوعية: | Fault detection, Machine learning, Industrial chemical processes, Safety process management, DICA-DCCA approach, CSTR framework, Chemical engineering, TP155-156, Information technology, T58.5-58.64 |
| الوصف: | Unexpected accidents and events in industrial chemical processes have resulted in a considerable number of causalities and property damage. Safety process management in industrial chemical processes is critical to avoid and ensure casualties and property damage. However, due to the immense scope and high complexity of current industrial chemical processes, the traditional safety process management approaches cannot address these challenges to attain adequate fault detection accuracy. To address this issue, an innovative machine learning-based distributed canonical correlation analysis-dynamic independent component analysis (DICA-DCCA) approach is needed to improve the fault detection effectiveness of complicated systems. The (DICA-DCCA) model could potentially detect anomalies and faults in industrial chemical data by utilizing three essential statistics:Id2,Ie2and squared prediction error (SPE). The practical effectiveness of the proposed frameworks is evaluated and compared using a continuous stirred tank reactor (CSTR) framework as a standard benchmark study. The research findings present that the suggested (DICA-DCCA) approach is more resilient and effective in detecting abnormalities and faults than the ICA and DICA approaches with FDR 100 % and FAR 0 %. The implied research approach is robust, operational, and productive. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2772-5081 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2772508124000188; https://doaj.org/toc/2772-5081 |
| DOI: | 10.1016/j.dche.2024.100156 |
| URL الوصول: | https://doaj.org/article/0f225195c0b249e9a1a0b342c3d330ab |
| رقم الانضمام: | edsdoj.0f225195c0b249e9a1a0b342c3d330ab |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 27725081 |
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| DOI: | 10.1016/j.dche.2024.100156 |