Advanced Statistical and Meta-Heuristic Based Optimization Fault Diagnosis Techniques in Complex Industrial Processes: A Comparative Analysis
Industrial processes are nonlinear and complicated in nature, requiring accurate fault detection to minimize the deterioration in performance and to respond quickly to emergencies. This work investigates industrial process defect identification and isolation, which is analytically difficult owing to...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10256179/ |
| _version_ | 1797660112898228224 |
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| author | Faizan E. Mustafa Abdul Qayyum Khan Abdus Samee Ijaz Ahmed Muhammad Abid Mohammad M. Alqahtani Muhammad Khalid |
| author_facet | Faizan E. Mustafa Abdul Qayyum Khan Abdus Samee Ijaz Ahmed Muhammad Abid Mohammad M. Alqahtani Muhammad Khalid |
| author_sort | Faizan E. Mustafa |
| collection | DOAJ |
| description | Industrial processes are nonlinear and complicated in nature, requiring accurate fault detection to minimize the deterioration in performance and to respond quickly to emergencies. This work investigates industrial process defect identification and isolation, which is analytically difficult owing to their complexity. This paper carefully analyzes four design methods for flaw identification and isolation based on Principal Component Analysis (PCA), Fisher Discriminant Analysis (FDA), Kernel Fisher Discriminant Analysis (KFDA), and Sequential quadratic programming (SQP). Our study includes the Tennessee Eastman Process (TEP) and the Penicillin Fermentation Process (PFP), among other comparable methods. We assess the proposed fault detection and isolation methods through detailed analysis and comparison. The simulation findings from our extensive investigation provide remarkable insights. Simulation findings show that FDA and KFDA work well in fault identification and isolation, but PCA has certain limits. We also considered SQP as a TEP fault detection and isolation improvement tool. SQP is noted for its success in nonlinear and restricted optimization problems, making it ideal for fault identification and isolation in complicated industrial processes. Data-driven design approaches increase problem identification in complicated industrial processes with greater reliability and efficiency than PCA-based methods. This study also shows that advanced data-driven techniques can improve industrial fault diagnosis, improving operational safety and system performance by leveraging the FDA, KFDA, and SQP. |
| first_indexed | 2024-03-11T18:26:14Z |
| format | Article |
| id | doaj.art-6e094000409a4531ba5f3218a2c3f04c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T18:26:14Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6e094000409a4531ba5f3218a2c3f04c2023-10-13T23:00:25ZengIEEEIEEE Access2169-35362023-01-011110437310439110.1109/ACCESS.2023.331751610256179Advanced Statistical and Meta-Heuristic Based Optimization Fault Diagnosis Techniques in Complex Industrial Processes: A Comparative AnalysisFaizan E. Mustafa0Abdul Qayyum Khan1https://orcid.org/0000-0002-4153-0548Abdus Samee2Ijaz Ahmed3https://orcid.org/0000-0003-1308-8374Muhammad Abid4https://orcid.org/0000-0002-9904-9925Mohammad M. Alqahtani5https://orcid.org/0000-0003-0582-6867Muhammad Khalid6https://orcid.org/0000-0001-7779-5348Pakistan Institute of Engineering and Applied Sciences, Islamabad, PakistanPakistan Institute of Engineering and Applied Sciences, Islamabad, PakistanPakistan Institute of Engineering and Applied Sciences, Islamabad, PakistanPakistan Institute of Engineering and Applied Sciences, Islamabad, PakistanPakistan Institute of Engineering and Applied Sciences, Islamabad, PakistanDepartment of Industrial Engineering, King Khalid University, Abha, Saudi ArabiaElectrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi ArabiaIndustrial processes are nonlinear and complicated in nature, requiring accurate fault detection to minimize the deterioration in performance and to respond quickly to emergencies. This work investigates industrial process defect identification and isolation, which is analytically difficult owing to their complexity. This paper carefully analyzes four design methods for flaw identification and isolation based on Principal Component Analysis (PCA), Fisher Discriminant Analysis (FDA), Kernel Fisher Discriminant Analysis (KFDA), and Sequential quadratic programming (SQP). Our study includes the Tennessee Eastman Process (TEP) and the Penicillin Fermentation Process (PFP), among other comparable methods. We assess the proposed fault detection and isolation methods through detailed analysis and comparison. The simulation findings from our extensive investigation provide remarkable insights. Simulation findings show that FDA and KFDA work well in fault identification and isolation, but PCA has certain limits. We also considered SQP as a TEP fault detection and isolation improvement tool. SQP is noted for its success in nonlinear and restricted optimization problems, making it ideal for fault identification and isolation in complicated industrial processes. Data-driven design approaches increase problem identification in complicated industrial processes with greater reliability and efficiency than PCA-based methods. This study also shows that advanced data-driven techniques can improve industrial fault diagnosis, improving operational safety and system performance by leveraging the FDA, KFDA, and SQP.https://ieeexplore.ieee.org/document/10256179/Principal component analysisfisher discriminant analysiskernal fisher discriminant analysisfault detection and isolation and meta-heuristic optimization |
| spellingShingle | Faizan E. Mustafa Abdul Qayyum Khan Abdus Samee Ijaz Ahmed Muhammad Abid Mohammad M. Alqahtani Muhammad Khalid Advanced Statistical and Meta-Heuristic Based Optimization Fault Diagnosis Techniques in Complex Industrial Processes: A Comparative Analysis IEEE Access Principal component analysis fisher discriminant analysis kernal fisher discriminant analysis fault detection and isolation and meta-heuristic optimization |
| title | Advanced Statistical and Meta-Heuristic Based Optimization Fault Diagnosis Techniques in Complex Industrial Processes: A Comparative Analysis |
| title_full | Advanced Statistical and Meta-Heuristic Based Optimization Fault Diagnosis Techniques in Complex Industrial Processes: A Comparative Analysis |
| title_fullStr | Advanced Statistical and Meta-Heuristic Based Optimization Fault Diagnosis Techniques in Complex Industrial Processes: A Comparative Analysis |
| title_full_unstemmed | Advanced Statistical and Meta-Heuristic Based Optimization Fault Diagnosis Techniques in Complex Industrial Processes: A Comparative Analysis |
| title_short | Advanced Statistical and Meta-Heuristic Based Optimization Fault Diagnosis Techniques in Complex Industrial Processes: A Comparative Analysis |
| title_sort | advanced statistical and meta heuristic based optimization fault diagnosis techniques in complex industrial processes a comparative analysis |
| topic | Principal component analysis fisher discriminant analysis kernal fisher discriminant analysis fault detection and isolation and meta-heuristic optimization |
| url | https://ieeexplore.ieee.org/document/10256179/ |
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