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Chunhui Zhao
Researcher at Zhejiang University
Publications - 285
Citations - 5685
Chunhui Zhao is an academic researcher from Zhejiang University. The author has contributed to research in topics: Fault (power engineering) & Computer science. The author has an hindex of 36, co-authored 245 publications receiving 3703 citations. Previous affiliations of Chunhui Zhao include Chinese Ministry of Education & Hong Kong University of Science and Technology.
Papers
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Journal ArticleDOI
A full-condition monitoring method for nonstationary dynamic chemical processes with cointegration and slow feature analysis
Chunhui Zhao,Biao Huang +1 more
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Stage-based soft-transition multiple PCA modeling and on-line monitoring strategy for batch processes
TL;DR: In this paper, a soft transition multiple PCA (STMPCA) model is proposed to detect process transition by analyzing changes in the loading matrices, which reveal evolvement of the underlying process behaviours.
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Broad Convolutional Neural Network Based Industrial Process Fault Diagnosis With Incremental Learning Capability
Wanke Yu,Chunhui Zhao +1 more
TL;DR: Experimental results illustrate that the proposed broad convolutional neural network (BCNN) can better capture the characteristics of the fault process, and effectively update diagnosis model to include new coming abnormal samples, and fault classes.
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Fault Description Based Attribute Transfer for Zero-Sample Industrial Fault Diagnosis
Liangjun Feng,Chunhui Zhao +1 more
TL;DR: The idea of zero-shot learning into the industry field is introduced, and the zero-sample fault diagnosis task is tackled by proposing the fault description based attribute transfer method, showing that it is indeed possible to diagnose target faults without their samples.
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Fault-relevant Principal Component Analysis (FPCA) method for multivariate statistical modeling and process monitoring
Chunhui Zhao,Furong Gao +1 more
TL;DR: A fault-relevant principal component analysis (FPCA) algorithm is proposed for statistical modeling and process monitoring by using both normal and fault data and provides a detailed insight into the decomposition of the original normal process information from the fault- relevant perspective.