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Found 6 papers, 0 papers with code
Autoencoder-assisted Feature Ensemble Net for Incipient Faults
Deep learning has shown the great power in the field of fault detection.
Continual learning-based probabilistic slow feature analysis for multimode dynamic process monitoring
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.
Self-learning sparse PCA for multimode process monitoring
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 the learned knowledge of previous modes.
Monitoring nonstationary processes based on recursive cointegration analysis and elastic weight consolidation
In this paper, recursive cointegration analysis (RCA) is first proposed to distinguish the real faults from normal systems changes, where the model is updated once a new normal sample arrives and can adapt to slow change of cointegration relationship.
Monitoring multimode processes: a modified PCA algorithm with continual learning ability
The computational complexity and key parameters are discussed to further understand the relationship between PCA and the proposed algorithm.
Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation
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 monitoring.
