Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Yuri A.W. Shardt"'
Publikováno v:
Engineering, Vol 19, Iss , Pp 240-251 (2022)
Recently developed fault classification methods for industrial processes are mainly data-driven. Notably, models based on deep neural networks have significantly improved fault classification accuracy owing to the inclusion of a large number of data
Externí odkaz:
https://doaj.org/article/b0e61632ab704a73928c783692c67d5d
Autor:
Xinrui Gao, Yuri A.W. Shardt
Publikováno v:
IFAC-PapersOnLine. 55:43-48
Autor:
Benjamin Jahn, Yuri A.W. Shardt
Publikováno v:
IFAC-PapersOnLine. 55:562-567
Publikováno v:
IFAC-PapersOnLine. 55:786-791
Autor:
Yuri A.W. Shardt, Xinrui Gao
Publikováno v:
Journal of Process Control. 105:27-47
Modern industrial processes are large-scale, highly complex systems with many units and equipment. The complex flow of mass and energy, as well as the compensation effects of closed-loop control systems, cause significant cross-correlation and autoco
Publikováno v:
2022 IEEE International Symposium on Advanced Control of Industrial Processes (AdCONIP).
Autor:
Xinrui Gao, Yuri A.W. Shardt
Publikováno v:
2022 IEEE International Symposium on Advanced Control of Industrial Processes (AdCONIP).
Publikováno v:
Journal of the Taiwan Institute of Chemical Engineers. 122:14-22
Driven by the strong demand for sparsity in dimensional reduction techniques, a sparse modeling and monitoring approach based on sparse, distributed principal component analysis (SDPCA) is proposed to achieve sparsity. To this end, the data set is fi
Publikováno v:
IEEE Transactions on Industrial Electronics. 68:4404-4414
Industrial process data are naturally complex time series with high nonlinearities and dynamics. To model nonlinear dynamic processes, a long short-term memory (LSTM) network is very suitable for soft sensor model development. However, the original L
Publikováno v:
Control Engineering Practice. 132:105405