Reliable Fault Detection and Diagnosis of Large-Scale Nonlinear Uncertain Systems Using Interval Reduced Kernel PLS
Kernel partial least squares (KPLS) models are widely used as nonlinear data-driven methods for faults detection (FD) in industrial processes. However, KPLS models lead to irrelevant performance over long operation periods due to process parameters changes, errors and uncertainties associated with m...
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9076626/ |
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| author | Radhia Fezai Kamaleldin Abodayeh Majdi Mansouri Abdelmalek Kouadri Mohamed-Faouzi Harkat Hazem Nounou Mohamed Nounou Hassani Messaoud |
| author_facet | Radhia Fezai Kamaleldin Abodayeh Majdi Mansouri Abdelmalek Kouadri Mohamed-Faouzi Harkat Hazem Nounou Mohamed Nounou Hassani Messaoud |
| author_sort | Radhia Fezai |
| collection | DOAJ |
| description | Kernel partial least squares (KPLS) models are widely used as nonlinear data-driven methods for faults detection (FD) in industrial processes. However, KPLS models lead to irrelevant performance over long operation periods due to process parameters changes, errors and uncertainties associated with measurements. Therefore, in this paper, two different interval reduced KPLS (IRKPLS) models are developed for monitoring large scale nonlinear uncertain systems. The proposed IRKPLS models present an interval versions of the classical KPLS model. The two proposed IRKPLS models are based on the Euclidean distance between interval-valued observations as a dissimilarity metric to keep only the more relevant and informative samples. The first proposed IRKPLS technique uses the centers and ranges of intervals to estimate the interval model, while the second one is based on the upper and lower bounds of intervals for model identification. These obtained models are used to evaluate the monitored interval residuals. The aforementioned interval residuals are fed to the generalized likelihood ratio test (GLRT) chart to detect the faults. In addition to considering the uncertainties in the input-output systems, the new IRKPLS-based GLRT techniques aim to decrease the execution time when ensuring the fault detection performance. The developed IRKPLS-based GLRT approaches are evaluated across various faults of the well-known Tennessee Eastman (TE) process. The performance of the proposed IRKPLS-based GLRT methods is evaluated in terms of missed detection rate, false alarms rate, and execution time. The obtained results demonstrate the efficiency of the proposed approaches, compared with the classical interval KPLS. |
| first_indexed | 2024-12-16T16:57:14Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T16:57:14Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-caa9cd029c7f4c5184986f106d165c912022-12-21T22:23:50ZengIEEEIEEE Access2169-35362020-01-018783437835310.1109/ACCESS.2020.29899179076626Reliable Fault Detection and Diagnosis of Large-Scale Nonlinear Uncertain Systems Using Interval Reduced Kernel PLSRadhia Fezai0https://orcid.org/0000-0002-5281-8301Kamaleldin Abodayeh1https://orcid.org/0000-0003-0735-6520Majdi Mansouri2https://orcid.org/0000-0001-6390-4304Abdelmalek Kouadri3https://orcid.org/0000-0003-3201-2500Mohamed-Faouzi Harkat4https://orcid.org/0000-0003-2093-0902Hazem Nounou5https://orcid.org/0000-0001-8075-1581Mohamed Nounou6https://orcid.org/0000-0003-0520-9623Hassani Messaoud7https://orcid.org/0000-0002-6971-2298Electrical and Computer Engineering Program, Texas A&M University at Qatar, Doha, QatarDepartment of Mathematical Sciences, Prince Sultan University, Riyadh, Saudi ArabiaElectrical and Computer Engineering Program, Texas A&M University at Qatar, Doha, QatarSignals and Systems Laboratory, Institute of Electrical and Electronics Engineering, University M Hamed Bougara of Boumerdes, Boumerdes, AlgeriaLASMA, Badji Mokhtar - Annaba University, Annaba, AlgeriaElectrical and Computer Engineering Program, Texas A&M University at Qatar, Doha, QatarChemical Engineering Program, Texas A&M University at Qatar, Doha, QatarResearch Laboratory of Automation, Signal Processing and Image (LARATSI), National School of Engineering Monastir, Rue Ibn ELJazzar, Monastir, TunisiaKernel partial least squares (KPLS) models are widely used as nonlinear data-driven methods for faults detection (FD) in industrial processes. However, KPLS models lead to irrelevant performance over long operation periods due to process parameters changes, errors and uncertainties associated with measurements. Therefore, in this paper, two different interval reduced KPLS (IRKPLS) models are developed for monitoring large scale nonlinear uncertain systems. The proposed IRKPLS models present an interval versions of the classical KPLS model. The two proposed IRKPLS models are based on the Euclidean distance between interval-valued observations as a dissimilarity metric to keep only the more relevant and informative samples. The first proposed IRKPLS technique uses the centers and ranges of intervals to estimate the interval model, while the second one is based on the upper and lower bounds of intervals for model identification. These obtained models are used to evaluate the monitored interval residuals. The aforementioned interval residuals are fed to the generalized likelihood ratio test (GLRT) chart to detect the faults. In addition to considering the uncertainties in the input-output systems, the new IRKPLS-based GLRT techniques aim to decrease the execution time when ensuring the fault detection performance. The developed IRKPLS-based GLRT approaches are evaluated across various faults of the well-known Tennessee Eastman (TE) process. The performance of the proposed IRKPLS-based GLRT methods is evaluated in terms of missed detection rate, false alarms rate, and execution time. The obtained results demonstrate the efficiency of the proposed approaches, compared with the classical interval KPLS.https://ieeexplore.ieee.org/document/9076626/Kernel PLS (KPLS)interval KPLS (IKPLS)interval reduced KPLS (IRKPLS)fault detection (FD)uncertain systems |
| spellingShingle | Radhia Fezai Kamaleldin Abodayeh Majdi Mansouri Abdelmalek Kouadri Mohamed-Faouzi Harkat Hazem Nounou Mohamed Nounou Hassani Messaoud Reliable Fault Detection and Diagnosis of Large-Scale Nonlinear Uncertain Systems Using Interval Reduced Kernel PLS IEEE Access Kernel PLS (KPLS) interval KPLS (IKPLS) interval reduced KPLS (IRKPLS) fault detection (FD) uncertain systems |
| title | Reliable Fault Detection and Diagnosis of Large-Scale Nonlinear Uncertain Systems Using Interval Reduced Kernel PLS |
| title_full | Reliable Fault Detection and Diagnosis of Large-Scale Nonlinear Uncertain Systems Using Interval Reduced Kernel PLS |
| title_fullStr | Reliable Fault Detection and Diagnosis of Large-Scale Nonlinear Uncertain Systems Using Interval Reduced Kernel PLS |
| title_full_unstemmed | Reliable Fault Detection and Diagnosis of Large-Scale Nonlinear Uncertain Systems Using Interval Reduced Kernel PLS |
| title_short | Reliable Fault Detection and Diagnosis of Large-Scale Nonlinear Uncertain Systems Using Interval Reduced Kernel PLS |
| title_sort | reliable fault detection and diagnosis of large scale nonlinear uncertain systems using interval reduced kernel pls |
| topic | Kernel PLS (KPLS) interval KPLS (IKPLS) interval reduced KPLS (IRKPLS) fault detection (FD) uncertain systems |
| url | https://ieeexplore.ieee.org/document/9076626/ |
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