[1]ZHENG Jing,XIONG Weili.Multiblock k-nearest neighbor fault monitoring and diagnosis based on mutual information[J].CAAI Transactions on Intelligent Systems,2021,16(4):717-728.[doi:10.11992/tis.202007035]
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Multiblock k-nearest neighbor fault monitoring and diagnosis based on mutual information

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 4 Page number: 717-728 Column: 学术论文—智能系统 Public date: 2021-07-05
Title:
Multiblock k-nearest neighbor fault monitoring and diagnosis based on mutual information
Author(s):
ZHENG Jing1; 2;  XIONG Weili1; 2
1. China Key Laboratory of Advanced Process Control for Light Industry Ministry of Education, Jiangnan University, Wuxi 214122, China;
2. School of the Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Keywords:
mutual information; multi-block modeling; k-nearest neighbor; process monitoring; fault detection; Bayesian inference; fault diagnosis; Mahalanobis distance
CLC:
TP277
DOI:
10.11992/tis.202007035
Abstract:
The traditional k-nearest neighbor (kNN) fault monitoring does not take into account the process of local information and only builds a global model. Thus, a multi-block kNN fault monitoring algorithm based on mutual information is proposed. First, with the nonlinear and non-Gaussian characteristics of the modeled data taken into consideration, subblocks are constructed based on mutual information between variables. Then, the kNN algorithm is used to model and monitor each subblock, in which the kNN model reflects more local characteristics of the process. Lastly, the monitoring results of all subblocks are fused by the Bayesian inference method, and a fault diagnosis method based on Mahalanobis distance is used to identify the source of faults. Through the application simulation in the Tennessee Eastman process and the blast furnace ironmaking process, the monitoring results show the feasibility and effectiveness of the proposed method.
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