no code implementations • 19 Mar 2024 • Shijin Chen, Zeyi Liu, Xiao He, Dongliang Zou, Donghua Zhou
The gearbox is a critical component of electromechanical systems.
no code implementations • 23 Feb 2022 • Jingxin Zhang, Donghua Zhou, Maoyin Chen, Xia Hong
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.
no code implementations • 7 Aug 2021 • 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 importance of variables and a regularization term is added to preserve the learned knowledge of previous modes.
no code implementations • 21 Jan 2021 • Jingxin Zhang, Donghua Zhou, Maoyin Chen
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.
no code implementations • 13 Dec 2020 • Jingxin Zhang, Donghua Zhou, Maoyin Chen
The computational complexity and key parameters are discussed to further understand the relationship between PCA and the proposed algorithm.
no code implementations • 13 Dec 2020 • Jingxin Zhang, Maoyin Chen, Hao Chen, Xia Hong, Donghua Zhou
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.
no code implementations • 14 May 2020 • Yinghong Zhao, Xiao He, Donghua Zhou, Michael G. Pecht
Different from the existing moving average (MA) technique that puts an equal weight on samples within a time window, WMA uses correlation information to find an optimal weight vector (OWV), so as to better improve the index's robustness and sensitivity.