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Weichen Hao

As seen in: Wiley Online Library, MDPI
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Articles by Weichen Hao

Deep residual temporal independent variable analysis for nonlinear dynamic process monitoring

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By Xiang Li, Weichen Hao, Zhijiang Lou
| Wiley Online Library Verified
PEER REVIEW For transparency, the peer review documents associated with this article are available at https://doi.org/10.1002/cjce.70612.
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Monitoring dynamic process with orthonormal subspace analysis

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By Zhijiang Lou, Weichen Hao, Shan Lu, Yonghui Wang
| Wiley Online Library Verified
REFERENCES 1, , , , IEEE Transactions on Industrial Informatics 2021, 18, 1039. 2, , J. Process Control 2018, 67, 35. 3, , , , , IEEE Trans. Instrum. Meas. 2020, 69, 9535. 4, , , International Journal of Architectural Heritage 2022, 16, 1325. 5, , Control Engineering Practice 2018, 80, 17. 6, , , , , , ACS Omega 2022, 7, 37 248. 7, , , , Can. J. Chem. Eng. 2020, 98, 1307. 8, , Chemom. Intell. Lab. Syst. 2019, 185, 47. 9, , IEEE Transactions on Control Systems Technology 2019, 28, 2581.
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A Novel Dynamic Process Monitoring Algorithm: Dynamic Orthonormal Subspace Analysis

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By Shan Lu, Yonghui Wang, Weichen Hao, Zhijiang Lou
| MDPI
Abstract : Orthonormal subspace analysis (OSA) is proposed for handling the subspace decomposition issue and the principal component selection issue in traditional key performance indicator (KPI)-related process monitoring methods such as partial least squares (PLS) and canonical correlation analysis (CCA). However, it is not appropriate to apply the static OSA algorithm to a dynamic process since OSA pays no attention to the auto-correlation relationships in variables.
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