97 citations · 174 across the 9 of their papers we have counts for
9 papers
SCCAM: Supervised Contrastive Convolutional Attention Mechanism for Ante-hoc Interpretable Fault Diagnosis with Limited Fault Samples
Mengxuan Li, Peng Peng, Jingxin Zhang +2
In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely…
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…
Structure Parameter Optimized Kernel Based Online Prediction with a Generalized Optimization Strategy for Nonstationary Time Series
Jinhua Guo, Hao Chen, Jingxin Zhang +1
In this paper, sparsification techniques aided online prediction algorithms in a reproducing kernel Hilbert space are studied for nonstationary time series. The online prediction a…
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…
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…
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…
An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring
Jingxin Zhang, Hao Chen, Songhang Chen +1
An improved mixture of probabilistic principal component analysis (PPCA) has been introduced for nonlinear data-driven process monitoring in this paper. To realize this purpose, th…
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…
Graph learning under spectral sparsity constraints
B. Subbareddy, Aditya Siripuram, Jingxin Zhang
Graph inference plays an essential role in machine learning, pattern recognition, and classification. Signal processing based approaches in literature generally assume some variati…