Fault Detection Method Using Auto-Associative Shared Nearest Neighbor Kernel Regression for Industrial Processes
As industrial systems grow larger and more interconnected, timely fault detection is essential to minimize downtime, enhance reliability, and reduce costs. However, conventional methods focus on reactive maintenance, limiting their ability to detect faults before escalation. Additionally, fault prop...
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| Format: | Article |
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MDPI AG
2025-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/5/2251 |
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| author | Minseok Kim Eunkyeong Kim Seunghwan Jung Baekcheon Kim Jinyong Kim Sungshin Kim |
| author_facet | Minseok Kim Eunkyeong Kim Seunghwan Jung Baekcheon Kim Jinyong Kim Sungshin Kim |
| author_sort | Minseok Kim |
| collection | DOAJ |
| description | As industrial systems grow larger and more interconnected, timely fault detection is essential to minimize downtime, enhance reliability, and reduce costs. However, conventional methods focus on reactive maintenance, limiting their ability to detect faults before escalation. Additionally, fault propagation in large-scale systems can degrade detection performance. To address these challenges, we propose an auto-associative shared nearest neighbor kernel regression method for fault detection in complex industrial processes. Inspired by attention mechanisms, the proposed approach assigns higher weights to relevant training data. Shared nearest neighbor is used to assess similarity between faults and training data, rescaling distances accordingly. These adjusted distances are then utilized in auto-associative kernel regression for fault detection. The performance of the proposed method is evaluated by applying it to benchmark data from the Tennessee Eastman Process and a real-world, unplanned shutdown case concerning a circulating fluidized bed boiler. The experimental results show that the proposed method can detect anomalies up to 2 h earlier than conventional fault detection methods. |
| format | Article |
| id | doaj-art-e5d17876da8a41edb56a0bad5a4406ed |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-e5d17876da8a41edb56a0bad5a4406ed2025-08-20T02:05:23ZengMDPI AGApplied Sciences2076-34172025-02-01155225110.3390/app15052251Fault Detection Method Using Auto-Associative Shared Nearest Neighbor Kernel Regression for Industrial ProcessesMinseok Kim0Eunkyeong Kim1Seunghwan Jung2Baekcheon Kim3Jinyong Kim4Sungshin Kim5Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of KoreaAs industrial systems grow larger and more interconnected, timely fault detection is essential to minimize downtime, enhance reliability, and reduce costs. However, conventional methods focus on reactive maintenance, limiting their ability to detect faults before escalation. Additionally, fault propagation in large-scale systems can degrade detection performance. To address these challenges, we propose an auto-associative shared nearest neighbor kernel regression method for fault detection in complex industrial processes. Inspired by attention mechanisms, the proposed approach assigns higher weights to relevant training data. Shared nearest neighbor is used to assess similarity between faults and training data, rescaling distances accordingly. These adjusted distances are then utilized in auto-associative kernel regression for fault detection. The performance of the proposed method is evaluated by applying it to benchmark data from the Tennessee Eastman Process and a real-world, unplanned shutdown case concerning a circulating fluidized bed boiler. The experimental results show that the proposed method can detect anomalies up to 2 h earlier than conventional fault detection methods.https://www.mdpi.com/2076-3417/15/5/2251fault detectionshared neighborauto-associative kernel regressionfluidized bed boiler |
| spellingShingle | Minseok Kim Eunkyeong Kim Seunghwan Jung Baekcheon Kim Jinyong Kim Sungshin Kim Fault Detection Method Using Auto-Associative Shared Nearest Neighbor Kernel Regression for Industrial Processes Applied Sciences fault detection shared neighbor auto-associative kernel regression fluidized bed boiler |
| title | Fault Detection Method Using Auto-Associative Shared Nearest Neighbor Kernel Regression for Industrial Processes |
| title_full | Fault Detection Method Using Auto-Associative Shared Nearest Neighbor Kernel Regression for Industrial Processes |
| title_fullStr | Fault Detection Method Using Auto-Associative Shared Nearest Neighbor Kernel Regression for Industrial Processes |
| title_full_unstemmed | Fault Detection Method Using Auto-Associative Shared Nearest Neighbor Kernel Regression for Industrial Processes |
| title_short | Fault Detection Method Using Auto-Associative Shared Nearest Neighbor Kernel Regression for Industrial Processes |
| title_sort | fault detection method using auto associative shared nearest neighbor kernel regression for industrial processes |
| topic | fault detection shared neighbor auto-associative kernel regression fluidized bed boiler |
| url | https://www.mdpi.com/2076-3417/15/5/2251 |
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