A novel fault classification method using reconstructed distance‐based discriminant locality preserving projection for industrial processes

Abstract

AbstractFault detection and classification is a crucial issue in modern industrial processes for ensuring steady operation and high product quality. The process data collected and stored fully reflect the equipment running state and the production process. Moreover, the extracted nonlinear features can directly affect the effectiveness of the data‐driven fault classification model. In this paper, a novel fault classification method based on nonlinear feature extraction using reconstructed distance‐based discriminant locality preserving projection (RD‐DLPP) is proposed. First, a hypersphere model for each class of data is developed according to the spatial structures and classes information in high‐dimensional space. The hyperspheres are used as indicators to evaluate the discriminatory difficulty of samples. Second, the constraints of the correlations between the k‐nearest neighbour points of the sample and the hypersphere are introduced, which can efficiently reconstruct new measure metrics between the sample and its k‐nearest neighbour points. Finally, an improved fault classification model based on RD‐DLPP is established for the construction of the highly discriminant subspace. The Bayesian decision is then used to classify the samples. The feasibility and efficiency of the proposed method are verified by the Tennessee Eastman process as a case study.