J
Jinlin Zhu
Researcher at Zhejiang University
Publications - 25
Citations - 987
Jinlin Zhu is an academic researcher from Zhejiang University. The author has contributed to research in topics: Mixture model & Gaussian. The author has an hindex of 13, co-authored 24 publications receiving 720 citations. Previous affiliations of Jinlin Zhu include Hong Kong University of Science and Technology & Nanyang Technological University.
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Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes With Big Data
TL;DR: A systematic fault detection and isolation scheme is designed so that the whole large-scale process can be hierarchically monitored from the plant-wide level, unit block level, and variable level and the effectiveness of the proposed method is evaluated.
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Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data
TL;DR: A systematic review of various state-of-the-art data preprocessing tricks as well as robust principal component analysis methods for process understanding and monitoring applications and big data perspectives on potential challenges and opportunities have been highlighted.
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Large-scale plant-wide process modeling and hierarchical monitoring: A distributed Bayesian network approach
TL;DR: Simulation results on the plant-wide Tennessee Eastman process show that the distributed Bayesian network approach can be feasible for modeling large-scale process and provides informative multi-level reference results for further diagnosis and isolation.
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HMM-Driven Robust Probabilistic Principal Component Analyzer for Dynamic Process Fault Classification
TL;DR: A novel hidden Markov model (HMM)-driven robust latent variable model (LVM) is proposed for fault classification in dynamic industrial processes and a robust probabilistic model with Student's t mixture output is designed for tolerating outliers.
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Robust modeling of mixture probabilistic principal component analysis and process monitoring application
TL;DR: A Bayesian soft decision fusion strategy is developed which is combined with the robust local monitoring models under different operating conditions and shows enhanced modeling and monitoring performance in both outlier and missing data cases, compared to the mixture probabilistic principal analysis model.