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

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 (kNN), 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.

Fault Detection Method based on Incrementable Laplacian Eigenmaps and Normal Space

“…Both LOF and FD-KNN determine whether a sample is a fault point by the outlier degree of the sample, without considering whether the structure of the process data presents nonlinearity, therefore works well when monitoring nonlinear processes. LOF and FD-KNN perform well in many applications in the process monitoring domain (Guo et al, 2018;Kim et al, 2022), however, the methods do not involve dimensionality reduction and therefore will incur high computational costs when the dataset dimensionality is too high. In this regard, ZHOU et al improved it by applying Random Projections to the FD-KNN method and proposed fault detection using Random Projections and k-Nearest Neighbor Rule (RP-KNN) (Zhou et al, 2014).…”