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

Abstract

Mode changes in today's sophisticated industrial processes result in unanticipated shutdowns, which could reduce the lifespan of critical machinery and result in high maintenance costs. A technique that can identify the fault of equipment operating in various modes is needed to prevent this issue. In light of this, we suggest a novel fault detection technique that makes use of the k-nearest neighbor normalization-based weight local outlier factor (WLOF). The suggested approach carries out local normalization using neighbors to take into account potential 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, conventional LOF is greatly affected by the distance between its neighbors, performance may deteriorate when faulty data is adjacent to normal data. To improve the defect detection performance of LOF, the proposed method multiplies proportionally by the distance between each neighbor. A circulating fluidized bed boiler and a multimode numerical case were used to assess the effectiveness of the suggested method. The results of the experiments demonstrate that the suggested method performs better than conventional PCA, kernel PCA (KPCA), k-nearest neighbor (kNN), and LOF. In particular, compared to conventional techniques, the suggested method increased detection accuracy by 20%. Therefore, the proposed technique is applicable to a real process operating in multiple modes.