Fault Detection Method Using Auto-Associative Shared Nearest Neighbor Kernel Regression for Industrial Processes DOI Creative Commons
Minseok Kim, Eun Kyeong Kim,

Seunghwan Jung

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2251 - 2251

Published: Feb. 20, 2025

As industrial systems grow larger and more interconnected, timely fault detection is essential to minimize downtime, enhance reliability, reduce costs. However, conventional methods focus on reactive maintenance, limiting their ability detect faults before escalation. Additionally, propagation in large-scale can degrade performance. To address these challenges, we propose an auto-associative shared nearest neighbor kernel regression method for complex processes. Inspired by attention mechanisms, the proposed approach assigns higher weights relevant training data. Shared used assess similarity between data, rescaling distances accordingly. These adjusted are then utilized detection. The performance of evaluated applying it benchmark data from Tennessee Eastman Process a real-world, unplanned shutdown case concerning circulating fluidized bed boiler. experimental results show that anomalies up 2 h earlier than methods.

Language: Английский

Model Interpretation and Interpretability Performance Evaluation of Graph Convolutional Network Fault Diagnosis for Air Handling Units DOI
Guannan Li, Zhang Le, Lingzhi Yang

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112048 - 112048

Published: Feb. 1, 2025

Language: Английский

Citations

0
Fault Detection Method Using Auto-Associative Shared Nearest Neighbor Kernel Regression for Industrial Processes DOI Creative Commons
Minseok Kim, Eun Kyeong Kim,

Seunghwan Jung

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2251 - 2251

Published: Feb. 20, 2025

As industrial systems grow larger and more interconnected, timely fault detection is essential to minimize downtime, enhance reliability, reduce costs. However, conventional methods focus on reactive maintenance, limiting their ability detect faults before escalation. Additionally, propagation in large-scale can degrade performance. To address these challenges, we propose an auto-associative shared nearest neighbor kernel regression method for complex processes. Inspired by attention mechanisms, the proposed approach assigns higher weights relevant training data. Shared used assess similarity between data, rescaling distances accordingly. These adjusted are then utilized detection. The performance of evaluated applying it benchmark data from Tennessee Eastman Process a real-world, unplanned shutdown case concerning circulating fluidized bed boiler. experimental results show that anomalies up 2 h earlier than methods.

Language: Английский

Citations

0