Abstract
Data-driven fault detection and identification methods are important in large-scale chemical processes. However, some traditional methods often fail to show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian distribution, and multi-operating mode. To cope with these issues, the k-NN (k-Nearest Neighbor) fault detection method and extensions have been developed in recent years. Nevertheless, these methods are primarily used for fault detection, and few papers can be found that examine fault identification. In this paper, in order to extract effective fault information, the relationship between various faults and abnormal variables is studied, and an accurate “fault–symptom” table is presented. Then, a novel fault identification method based on k-NN variable contribution and CNN data reconstruction theories is proposed. When there is an abnormality, a variable contribution plot method based on k-NN is used to calculate the contribution index of each variable, and the feasibility of this method is verified by contribution decomposition theory, which includes a feasibility analysis of a single abnormal variable and multiple abnormal variables. Furthermore, to identify all the faulty variables, a CNN (Center-based Nearest Neighbor) data reconstruction method is proposed; the variables that have the larger contribution indices can be reconstructed using the CNN reconstruction method in turn. The proposed search strategy can guarantee that all faulty variables are found in each sample. The reliability and validity of the proposed method are verified by a numerical example and the Continuous Stirred Tank Reactor system.
Highlights
In modern chemical processes, fault detection and identification have become important tasks to ensure process safety, product quality, and environmental sustainability [1,2,3]
This paper proposes a novel fault identification method based on k-Nearest Neighbor (k-NN) variable contribution and CNN data reconstruction methods for chemical processes
It can be seen that the principal component analysis (PCA) and reconstruction-based contribution (RBC) methods can show the contribution value of abnormal variables in the fault period, but they cannot avoid the diffusion effect caused by PCA data transformation
Summary
Fault detection and identification have become important tasks to ensure process safety, product quality, and environmental sustainability [1,2,3]. A reconstruction-based approach was proposed for isolating faulty variables from the subspace of faults [30], Carlos and Qin proposed a reconstruction-based contribution for process monitoring and fault diagnosis [31] These methods have been applied to reconstruct the data of faulty variables before performing a prediction for a soft sensor model. Fault identification for chemical processes on the basis of k-NN variable contribution and CNN data reconstruction methods has certain challenges, and it has certain academic research value and practical significance. This paper proposes a novel fault identification method based on k-NN variable contribution and CNN data reconstruction methods for chemical processes.
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