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• W3043134799 abstract "Most classical fault diagnosis methods such as principal component analysis (PCA), are extracting comprehensive information to represent data features in fault diagnosis. In comparison, Non-Negative Matrix Factorization (NMF) is a method for dimension reduction and feature extraction, and because its characteristic matrix has sparsity, this method is superior in the ability of extracting the local feature and suppressing noises; however, the NMF method is not applicable for dynamic industrial processes. In this paper, we introduce the past information of industrial processes for fault diagnosis, proposing Canonical Variate Analysis - Non-Negative Matrix Factorization (CVA-NMF) methods to improve the dynamic performance of NMF. The experimental results via TE process indicate that the proposed approach could handle a dynamic production process such as Fault 2 and Fault 5 and retain the superior performance of NMF." @default.
• W3043134799 created "2020-07-23" @default.
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• W3043134799 date "2020-07-20" @default.
• W3043134799 modified "2023-09-27" @default.
• W3043134799 title "An Improved Non-negative Matrix Factorization Method for Dynamic Industrial Fault Diagnosis" @default.
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• W3043134799 doi "https://doi.org/10.1252/jcej.19we143" @default.
• W3043134799 hasPublicationYear "2020" @default.
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