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      Fault Detection and Health Assessment of Equipment Based on Fuzzy DPCA Spatial Eigenvalue Similarity

      1 , 1 , 2
      Mathematical Problems in Engineering
      Hindawi Limited

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          Abstract

          To improve the fault recognition rate of the dynamic principal component spatial data drive method, a fault diagnosis and equipment health status assessment method based on similarity fuzzy dynamics principal component analysis was proposed. First, the data are fuzzified according to the error function, and an augmented matrix is constructed. The eigenvalues are decomposed to obtain a score matrix and residual matrix of the fuzzy principal component. Further, the similarity between fault data and normal data is calculated. Meanwhile, a health assessment of the equipment is realized. The contribution rate of the observed variables is calculated. Finally, general Tennessee Eastman data and health assessment of a hydraulic press are used to validate the algorithm. The results show that the SFDPCA has a 100 % fault recognition rate for some faults, and the recognition rate for other faults is also higher than that of DPCA-Diss, DPCA-SPE, and PCA-SPE. The SDDPCA accurately identifies abnormal phenomena. It can determine the health level of prefilling and effectively make up for the shortcomings of PCA T 2 , PCA-SPE, DPCA-Diss, and other methods and also can be applied to data-driven fault diagnosis to improve the fault recognition rate.

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          Most cited references34

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          A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

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            A Novel Gate Resource Allocation Method Using Improved PSO-Based QEA

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              An Enhanced MSIQDE Algorithm With Novel Multiple Strategies for Global Optimization Problems

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                Author and article information

                Contributors
                Journal
                Mathematical Problems in Engineering
                Mathematical Problems in Engineering
                Hindawi Limited
                1563-5147
                1024-123X
                July 1 2021
                July 1 2021
                : 2021
                : 1-17
                Affiliations
                [1 ]College of Mechanical and Electrical Engineering, Huzhou Vocational and Technical College, Huzhou 313000, China
                [2 ]School of Engineering, Huzhou University, Huzhou 313000, China
                Article
                10.1155/2021/9983497
                3030c27c-f161-4364-ab5e-c61d7110dfdf
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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