Journal article

Novel Pattern-Matching Integrated KCVA with Adaptive Rank-Order Morphological Filter and Its Application to Fault Diagnosis

Y Xu, C Fan, QX Zhu, A Rajabifard, N Chen, Y Chen, YL He

Industrial & Engineering Chemistry Research | American Chemical Society | Published : 2020

Abstract

With the scale expansion of industrial processes, the relationship between process variables has become complex and highly nonlinear. As a result, the requirements for fault diagnosis and safety monitoring has become demanding. To address this problem, a novel and effective pattern-matching method using kernel canonical variate analysis (KCVA) integrated with an adaptive rank-order morphological filter (ARMF) is proposed for fault diagnosis. In the proposed method, KCVA is first used to extract the nonlinear correlation information with dynamic characteristics from the original process data and achieve feature dimension reduction; the features extracted by KCVA are then subjected to ARMF tra..

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Grants

Awarded by National Natural Science Foundation of China


Awarded by China Scholarship Council State-Sponsored Scholarship Program


Awarded by Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, WUHAN University


Awarded by Fundamental Research Funds for the Central Universities


Funding Acknowledgements

This work is supported by grants from the National Natural Science Foundation of China under Grant Nos. 61973022 and 61573051, the China Scholarship Council State-Sponsored Scholarship Program under Grant No. 201806885004, the Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, WUHAN University under Grant No. 18I01, and the Fundamental Research Funds for the Central Universities under Grant Nos. XK1802-4 and JD1914.