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of 330
pro vyhledávání: '"Chen, Maoyin"'
Deep learning has shown the great power in the field of fault detection. However, for incipient faults with tiny amplitude, the detection performance of the current deep learning networks (DLNs) is not satisfactory. Even if prior information about th
Externí odkaz:
http://arxiv.org/abs/2404.13941
Publikováno v:
In Neurocomputing 7 October 2024 601
In this paper, a novel multimode dynamic process monitoring approach is proposed by extending elastic weight consolidation (EWC) to probabilistic slow feature analysis (PSFA) in order to extract multimode slow features for online monitoring. EWC was
Externí odkaz:
http://arxiv.org/abs/2202.11295
Traditional process monitoring methods, such as PCA, PLS, ICA, MD et al., are strongly dependent on continuous variables because most of them inevitably involve Euclidean or Mahalanobis distance. With industrial processes becoming more and more compl
Externí odkaz:
http://arxiv.org/abs/2110.09704
This paper proposes a novel sparse principal component analysis algorithm with self-learning ability for successive modes, where synaptic intelligence is employed to measure the importance of variables and a regularization term is added to preserve t
Externí odkaz:
http://arxiv.org/abs/2108.03449
This paper considers the problem of nonstationary process monitoring under frequently varying operating conditions. Traditional approaches generally misidentify the normal dynamic deviations as faults and thus lead to high false alarms. Besides, they
Externí odkaz:
http://arxiv.org/abs/2101.08579
Publikováno v:
Industrial and Engineering Chemistry Research, 58(14), 5579-5587, 2019
By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process m
Externí odkaz:
http://arxiv.org/abs/2012.07021
Publikováno v:
IEEE/CAA J. Autom. Sinica, vol. 9, no. 4, pp. 1-9, Apr. 2022
With the increasing intelligence and integration, a great number of two-valued variables (generally stored in the form of 0 or 1 value) often exist in large-scale industrial processes. However, these variables cannot be effectively handled by traditi
Externí odkaz:
http://arxiv.org/abs/2012.07230
For multimode processes, one generally establishes local monitoring models corresponding to local modes. However, the significant features of previous modes may be catastrophically forgotten when a monitoring model for the current mode is built. It w
Externí odkaz:
http://arxiv.org/abs/2012.07044
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