Paper
6 February 2022 Fault feature fusion based on entropy-weighted nuisance attribute projection and orthogonal locality preserving projection
Di Yang, Yong Lv, Rui Yuan, Zhang Dang
Author Affiliations +
Proceedings Volume 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021); 1208109 (2022) https://doi.org/10.1117/12.2623849
Event: Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), 2021, Chongqing, China
Abstract
Traditional feature fusion methods are difficult to extract sensitive features related to fault under various operating conditions. A new feature fusion method is proposed in this paper, which uses the combination of entropy-weighted nuisance attribute projection (EWNAP) and orthogonal locality preserving projections (OLPP) to extract new features. The clustering performance of the proposed method is macroscopically described by the scatter plot graph, and the new feature vectors processed by EWNAP-OLPP as the samples put into the back propagation (BP) neural network for fault pattern recognition.
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Di Yang, Yong Lv, Rui Yuan, and Zhang Dang "Fault feature fusion based on entropy-weighted nuisance attribute projection and orthogonal locality preserving projection", Proc. SPIE 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), 1208109 (6 February 2022); https://doi.org/10.1117/12.2623849
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KEYWORDS
Neural networks

Principal component analysis

Matrices

Pattern recognition

Data acquisition

Fuzzy logic

Statistical analysis

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