A novel industrial process fault monitoring method based on kernel robust non-negative matrix factorization
Author:
Funder
National Natural Science Foundation of China
Publisher
IOP Publishing
Subject
Applied Mathematics,Instrumentation,Engineering (miscellaneous)
Link
https://iopscience.iop.org/article/10.1088/1361-6501/ac0de2/pdf
Reference33 articles.
1. Process fault detection based on skew Gaussian distribution transformation and canonical variable analysis method;Guo,2019
2. Fault monitoring based on adaptive partition non-negative matrix factorization for non-Gaussian processes;Wang;IEEE Access,2019
3. A deep non-negative matrix factorization approach via autoencoder for nonlinear fault detection;Ren;IEEE Trans. Ind. Inf.,2019
4. Improved local cepstrum and its applications for gearbox and rolling bearing fault detection;Zhang;Meas. Sci. Technol.,2019
5. A data-driven group-sparse feature extraction method for fault detection of wind turbine transmission system;He;Meas. Sci. Technol.,2020
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