K
Kai Zhang
Researcher at University of Science and Technology Beijing
Publications - 52
Citations - 1573
Kai Zhang is an academic researcher from University of Science and Technology Beijing. The author has contributed to research in topics: Fault detection and isolation & Fault (power engineering). The author has an hindex of 19, co-authored 49 publications receiving 1162 citations. Previous affiliations of Kai Zhang include University of Duisburg-Essen & Chinese Ministry of Education.
Papers
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A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches ☆
TL;DR: The key performance indicator-based multivariate statistical process monitoring and fault diagnosis methods for linear static processes are surveyed and evaluated using the multivariate statistics framework and are broadly classified into three categories: direct, linear regression-based, and PLS-based.
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Canonical correlation analysis-based fault detection methods with application to alumina evaporation process
TL;DR: In this paper, canonical correlation analysis (CCA)-based fault detection methods are proposed for both static and dynamic processes, which are applied to an alumina evaporation process, and the achieved results show that both methods are applicable for fault detection.
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A Quality-Based Nonlinear Fault Diagnosis Framework Focusing on Industrial Multimode Batch Processes
TL;DR: It is shown using the real industrial data that for faults affecting the thickness and flatness of the strip steel in this process, the detection and diagnosis abilities are better compared with the existing methods.
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A Distributed Canonical Correlation Analysis-Based Fault Detection Method for Plant-Wide Process Monitoring
TL;DR: A new data-driven fault detection method based on distributed canonical correlation analysis (D-CCA) is proposed to address the plant-wide process monitoring problem and the false alarm rate, fault detection rate, and the detection delay are comparable, suggesting that the proposed method is feasible.
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Quality-Related Process Monitoring Based on Total Kernel PLS Model and Its Industrial Application
Kaixiang Peng,Kai Zhang,Gang Li +2 more
TL;DR: In this paper, a new total kernel projection to latent structures (T-KPLS) model was proposed for nonlinear quality-related process monitoring, which divides the input spaces into four parts instead of two parts in KPLS, where an individual subspace is responsible in predicting quality output, and two parts are utilized for monitoring the quality related variations.