Fault Detection Method Using Auto-Associative Shared Nearest Neighbor Kernel Regression for Industrial Processes
Guardado en:
| Publicado en: | Applied Sciences vol. 15, no. 5 (2025), p. 2251 |
|---|---|
| Autor principal: | |
| Otros Autores: | , , , , |
| Publicado: |
MDPI AG
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3176306249 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2076-3417 | ||
| 024 | 7 | |a 10.3390/app15052251 |2 doi | |
| 035 | |a 3176306249 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231338 |2 nlm | ||
| 100 | 1 | |a Kim, Minseok | |
| 245 | 1 | |a Fault Detection Method Using Auto-Associative Shared Nearest Neighbor Kernel Regression for Industrial Processes | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Distributed control systems | ||
| 653 | |a Failure | ||
| 653 | |a Statistical methods | ||
| 653 | |a Parameter estimation | ||
| 700 | 1 | |a Kim, Eunkyeong | |
| 700 | 1 | |a Jung, Seunghwan | |
| 700 | 1 | |a Kim, Baekcheon | |
| 700 | 1 | |a Kim, Jinyong | |
| 700 | 1 | |a Kim, Sungshin | |
| 773 | 0 | |t Applied Sciences |g vol. 15, no. 5 (2025), p. 2251 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3176306249/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3176306249/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3176306249/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |