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MK-DCCA Based Fault Diagnosis for Incipient Fault in Nonlinear Dynamic Processes
Version 1
: Received: 13 September 2023 / Approved: 13 September 2023 / Online: 14 September 2023 (07:20:45 CEST)
A peer-reviewed article of this Preprint also exists.
Wu, J.; Zhang, M.; Chen, L. MK-DCCA-Based Fault Diagnosis for Incipient Faults in Nonlinear Dynamic Processes. Processes 2023, 11, 2927. Wu, J.; Zhang, M.; Chen, L. MK-DCCA-Based Fault Diagnosis for Incipient Faults in Nonlinear Dynamic Processes. Processes 2023, 11, 2927.
Abstract
incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of Mixed Kernel Principal Components Analysis and Dynamic Canonical Correlation Analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, Comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient fault in nonlinear and dynamic system. Meanwhile, fault identification experiments were conducted, validating the high accuracy of fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance.
Keywords
dynamic system; incipient fault; process monitoring; fault detection; MKPCA; DCCA
Subject
Engineering, Control and Systems Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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