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20202022
most citedProcess monitoring based on orthogonal locality preserving projection with maximum likelihood estimation

18 citations · 19 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2022

Continual learning-based probabilistic slow feature analysis for multimode dynamic process monitoring

Jingxin Zhang, Donghua Zhou, Maoyin Chen +1

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 or…

cs.LG2021

Self-learning sparse PCA for multimode process monitoring

Jingxin Zhang, Donghua Zhou, Maoyin Chen

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 im…

eess.SY20211 cited

Monitoring nonstationary processes based on recursive cointegration analysis and elastic weight consolidation

Jingxin Zhang, Donghua Zhou, Maoyin Chen

This paper considers the problem of nonstationary process monitoring under frequently varying operating conditions. Traditional approaches generally misidentify the normal dynamic…

stat.ME202018 cited

Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation

Jingxin Zhang, Maoyin Chen, Hao Chen +2

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…

stat.ML2020

Monitoring multimode processes: a modified PCA algorithm with continual learning ability

Jingxin Zhang, Donghua Zhou, Maoyin Chen

For multimode processes, one generally establishes local monitoring models corresponding to local modes. However, the significant features of previous modes may be catastrophically…