Fault diagnosis based on variable-weighted kernel Fisher discriminant analysis |
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Authors: | Xiao Bin He Yu Pu Yang Ya Hong Yang |
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Affiliation: | aDepartment of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, Min Hang District, Shanghai, China;bDepartment of Automation, Nanchang University, Nanchang, Jiangxi Province, China |
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Abstract: | In this paper, a new fault diagnosis approach with variable-weighted kernel Fisher discriminant analysis (VW-KFDA) is proposed. The approach incorporates the variable weighting into KFDA. The variable weighting finds out the weight vector of each fault by maximizing separation between the normal and each fault data. With continuous non-negative values, each element of the weight vector represents the corresponding variable's contribution to a special fault. After all fault data are weighted by the corresponding weight vectors, KFDA is performed on these weighted fault data. These weight vectors offer important supplemental classification information to KFDA and effectively improve its multi-classification performance. The proposed approach is applied to the Tennessee Eastman process (TEP). The results show superior capability for fault diagnosis to KFDA and FDA. |
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Keywords: | Fault diagnosis Kernel Fisher discriminant analysis Variable weighting Classification |
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