A Hybrid Fault Detection Method of Independent Component Analysis and Auto-Associative Kernel Regression for Process Monitoring in Power Plant
In complex industrial processes, distributed control systems (DCSs) are currently operated to prevent unplanned shutdowns and major accidents. However, DCSs not only have the advantages of collecting large amounts of operational history data, but they also have the shortcoming of limited monitoring...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10906574/ |
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| author | Seunghwan Jung Jonggeun Kim Sungshin Kim |
| author_facet | Seunghwan Jung Jonggeun Kim Sungshin Kim |
| author_sort | Seunghwan Jung |
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| description | In complex industrial processes, distributed control systems (DCSs) are currently operated to prevent unplanned shutdowns and major accidents. However, DCSs not only have the advantages of collecting large amounts of operational history data, but they also have the shortcoming of limited monitoring capabilities, such as early detection, due to their reliance on generating fault alarms based on simple threshold values. To improve the stability and reliability of industrial processes, it is essential to operate DCS in conjunction with data-driven process monitoring technologies. In this paper, we propose a novel hybrid model combining independent component analysis (ICA) and auto-associative kernel regression (AAKR) to address the limitations of both models. The proposed model (ICA+AAKR) introduces a new method, cumulative percentage distance (CPD), which can determine the appropriate number of independent components (ICs) for dimensionality reduction in ICA. By inputting the dimension-reduced IC matrix into AAKR, the issue of excessive computation time caused by lazy learning in AAKR is effectively mitigated. We applied the proposed fault detection method to two well-known benchmarks (multivariate dynamic process and Tennessee Eastman process) and a real-world application (actual tube leakage in power plant) to verify its monitoring performance. The experimental results validated superior detection performance compared to existing methods for the two benchmark problems. In addition, the method demonstrated the potential to enhance process stability and reliability by enabling remarkable early detection of tube leakage in a circulating fluidized bed boiler at the power plant. |
| format | Article |
| id | doaj-art-6a87cadb1e854b0fb904e527cfb2e17c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-6a87cadb1e854b0fb904e527cfb2e17c2025-08-20T03:49:22ZengIEEEIEEE Access2169-35362025-01-0113391353915110.1109/ACCESS.2025.354645010906574A Hybrid Fault Detection Method of Independent Component Analysis and Auto-Associative Kernel Regression for Process Monitoring in Power PlantSeunghwan Jung0https://orcid.org/0000-0002-9387-393XJonggeun Kim1Sungshin Kim2https://orcid.org/0000-0003-4932-5458Department of Electrical and Electronics Engineering, Pusan National University, Busan, Republic of KoreaArtificial Intelligence Research Center, Korea Electrotechnology Research Institute, Changwon, Republic of KoreaDepartment of Electrical and Electronics Engineering, Pusan National University, Busan, Republic of KoreaIn complex industrial processes, distributed control systems (DCSs) are currently operated to prevent unplanned shutdowns and major accidents. However, DCSs not only have the advantages of collecting large amounts of operational history data, but they also have the shortcoming of limited monitoring capabilities, such as early detection, due to their reliance on generating fault alarms based on simple threshold values. To improve the stability and reliability of industrial processes, it is essential to operate DCS in conjunction with data-driven process monitoring technologies. In this paper, we propose a novel hybrid model combining independent component analysis (ICA) and auto-associative kernel regression (AAKR) to address the limitations of both models. The proposed model (ICA+AAKR) introduces a new method, cumulative percentage distance (CPD), which can determine the appropriate number of independent components (ICs) for dimensionality reduction in ICA. By inputting the dimension-reduced IC matrix into AAKR, the issue of excessive computation time caused by lazy learning in AAKR is effectively mitigated. We applied the proposed fault detection method to two well-known benchmarks (multivariate dynamic process and Tennessee Eastman process) and a real-world application (actual tube leakage in power plant) to verify its monitoring performance. The experimental results validated superior detection performance compared to existing methods for the two benchmark problems. In addition, the method demonstrated the potential to enhance process stability and reliability by enabling remarkable early detection of tube leakage in a circulating fluidized bed boiler at the power plant.https://ieeexplore.ieee.org/document/10906574/Tube leakagecirculating fluidized-bed boilerfault detectionprocess monitoringindependent component analysisauto-associative kernel regression |
| spellingShingle | Seunghwan Jung Jonggeun Kim Sungshin Kim A Hybrid Fault Detection Method of Independent Component Analysis and Auto-Associative Kernel Regression for Process Monitoring in Power Plant IEEE Access Tube leakage circulating fluidized-bed boiler fault detection process monitoring independent component analysis auto-associative kernel regression |
| title | A Hybrid Fault Detection Method of Independent Component Analysis and Auto-Associative Kernel Regression for Process Monitoring in Power Plant |
| title_full | A Hybrid Fault Detection Method of Independent Component Analysis and Auto-Associative Kernel Regression for Process Monitoring in Power Plant |
| title_fullStr | A Hybrid Fault Detection Method of Independent Component Analysis and Auto-Associative Kernel Regression for Process Monitoring in Power Plant |
| title_full_unstemmed | A Hybrid Fault Detection Method of Independent Component Analysis and Auto-Associative Kernel Regression for Process Monitoring in Power Plant |
| title_short | A Hybrid Fault Detection Method of Independent Component Analysis and Auto-Associative Kernel Regression for Process Monitoring in Power Plant |
| title_sort | hybrid fault detection method of independent component analysis and auto associative kernel regression for process monitoring in power plant |
| topic | Tube leakage circulating fluidized-bed boiler fault detection process monitoring independent component analysis auto-associative kernel regression |
| url | https://ieeexplore.ieee.org/document/10906574/ |
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