Fault Detection Method via k -Nearest Neighbor Normalization and Weight Local Outlier Factor for Circulating Fluidized Bed Boiler with Multimode Process

Minseok Kim, Seunghwan Jung, Baekcheon Kim, Jinyong Kim, Eunkyeong Kim, Jonggeun Kim and Sungshin Kim ()
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Minseok Kim: Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Korea
Seunghwan Jung: Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Korea
Baekcheon Kim: Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Korea
Jinyong Kim: Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Korea
Eunkyeong Kim: Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Korea
Jonggeun Kim: Artificial Intelligence Research Center, Korea Electrotechnology Research Institute, Changwon 51543, Korea
Sungshin Kim: Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Korea

Energies, 2022, vol. 15, issue 17, 1-21

Abstract: In modern complex industrial processes, mode changes cause unplanned shutdowns, potentially shortening the lifespan of key equipment and incurring significant maintenance costs. To avoid this problem, a method that can detect the fault of equipment operating in various modes is required. Therefore, we propose a novel fault detection method that uses the k -nearest neighbor normalization-based weight local outlier factor (WLOF). The proposed method performs local normalization using neighbors to consider possible mode changes in the normal data and WLOF is used for fault detection. In contrast to statistical methods, such as principal component analysis (PCA) and independent component analysis (ICA), the local outlier factor (LOF) uses the density of neighbors. However, because LOF is significantly affected by the distance between its neighbors, the weight is multiplied proportionally to the distance between each neighbor to improve the fault detection performance of the LOF. The efficiency of the proposed method was evaluated using a multimode numerical case and a circulating fluidized bed boiler. The experimental results show that the proposed method outperforms conventional PCA, kernel PCA (KPCA), k -nearest neighbor ( k NN), and LOF. In particular, the proposed method improved the detection accuracy by 20% compared with conventional methods. Therefore, the proposed method can be applied to a real process operating in multiple modes.

Keywords: fluidized bed boiler; fault detection; weighted normalization; local outlier factor (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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