References
Ge Z Q. Review on data-driven modeling and monitoring for plant-wide industrial processes. Chemometr Intell Lab Syst, 2017, 171: 16–25
Jiang Q C, Yan X F, Huang B. Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes. Ind Eng Chem Res, 2019, 58: 12899–12912
Du B, Xiong W, Wu J, et al. Stacked convolutional denoising auto-encoders for feature representation. IEEE Trans Cybern, 2017, 47: 1017–1027
Zhang Z, Jiang T, Li S, et al. Automated feature learning for nonlinear process monitoring — an approach using stacked denoising autoencoder and k-nearest neighbor rule. J Process Control, 2018, 64: 49–61
Wang K, Forbes M G, Gopaluni B, et al. Systematic development of a new variational autoencoder model based on uncertain data for monitoring nonlinear processes. IEEE Access, 2019, 7: 22554–22565
Lv F, Wen C, Liu M. Representation learning based adaptive multimode process monitoring. Chemometr Intell Lab Syst, 2018, 181: 95–104
Downs J J, Vogel E F. A plant-wide industrial process control problem. Comput Chem Eng, 1993, 17: 245–255
Acknowledgements
This work was supported by National Key Research and Development Program of China (Grant No. 2020YFA0908303) and National Natural Science Foundation of China (Grant No. 21878081).
Author information
Authors and Affiliations
Corresponding author
Additional information
Supporting information
Appendixes A–C. The supporting information is available online at info.scichina.com and link. springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.
Rights and permissions
About this article
Cite this article
Li, Z., Tian, L. & Yan, X. A novel deep quality-supervised regularized autoencoder model for quality-relevant fault detection. Sci. China Inf. Sci. 65, 159203 (2022). https://doi.org/10.1007/s11432-020-2964-7
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-020-2964-7