Quality oriented multimode processes monitoring based on a novel hierarchical common and specific structure with different order information

doi:10.1016/j.cjche.2020.09.067

中国化学工程学报 ›› 2021, Vol. 39 ›› Issue (11): 183-192.DOI: 10.1016/j.cjche.2020.09.067

• Process Systems Engineering and Process Safety • 上一篇下一篇

Quality oriented multimode processes monitoring based on a novel hierarchical common and specific structure with different order information

Yun Wang¹, Yuchen He², De Gu³

1 Mechanical and Electrical Engineering Department, Zhejiang Tongji Vocational College of Science and Technology, Hangzhou 311231, China;
2 College of Mechanical & Electrical Engineering, China Jiliang University, Hangzhou 310018, China;
3 School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China

收稿日期:2020-04-03 修回日期:2020-09-04 出版日期:2021-11-28 发布日期:2021-12-27
通讯作者: Yuchen He
基金资助:
This work is supported by the National Natural Science Foundation of China (61903352), China Postdoctoral Science Foundation (2020M671721), Zhejiang Province Natural Science Foundation of China (LQ19F030007), Natural Science Foundation of Jiangsu Province (BK20180594), Project of department of education of Zhejiang province (Y202044960),Project of Zhejiang Tongji Vocational College of Science and Technology (TRC1904) and Foundation of Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education, P.R. China, APCLI1803.

Quality oriented multimode processes monitoring based on a novel hierarchical common and specific structure with different order information

Yun Wang¹, Yuchen He², De Gu³

1 Mechanical and Electrical Engineering Department, Zhejiang Tongji Vocational College of Science and Technology, Hangzhou 311231, China;
2 College of Mechanical & Electrical Engineering, China Jiliang University, Hangzhou 310018, China;
3 School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China

Received:2020-04-03 Revised:2020-09-04 Online:2021-11-28 Published:2021-12-27
Contact: Yuchen He
Supported by:
This work is supported by the National Natural Science Foundation of China (61903352), China Postdoctoral Science Foundation (2020M671721), Zhejiang Province Natural Science Foundation of China (LQ19F030007), Natural Science Foundation of Jiangsu Province (BK20180594), Project of department of education of Zhejiang province (Y202044960),Project of Zhejiang Tongji Vocational College of Science and Technology (TRC1904) and Foundation of Key Laboratory of Advanced Process Control for Light Industry (Jiangnan University), Ministry of Education, P.R. China, APCLI1803.

摘要/Abstract

摘要： Due to higher demands on product diversity, flexible shift between productions of different products in one equipment becomes a popular solution, resulting in existence of multiple operation modes in a single process. In order to handle such multi-mode process, a novel double-layer structure is proposed and the original data are decomposed into common and specific characteristics according to the relationship between variables among each mode. In addition, both low and high order information are considered in each layer. The common and specific information within each mode can be captured and separated into several subspaces according to the different order information. The performance of the proposed method is further validated through a numerical example and the Tennessee Eastman (TE) benchmark. Compared with previous methods, superiority of the proposed method is validated by the better monitoring results.

关键词: Multimode processes monitoring, Dual iterations, Double layer information extraction, High order expansion, Quality related

Abstract: Due to higher demands on product diversity, flexible shift between productions of different products in one equipment becomes a popular solution, resulting in existence of multiple operation modes in a single process. In order to handle such multi-mode process, a novel double-layer structure is proposed and the original data are decomposed into common and specific characteristics according to the relationship between variables among each mode. In addition, both low and high order information are considered in each layer. The common and specific information within each mode can be captured and separated into several subspaces according to the different order information. The performance of the proposed method is further validated through a numerical example and the Tennessee Eastman (TE) benchmark. Compared with previous methods, superiority of the proposed method is validated by the better monitoring results.

Key words: Multimode processes monitoring, Dual iterations, Double layer information extraction, High order expansion, Quality related

Yun Wang, Yuchen He, De Gu. Quality oriented multimode processes monitoring based on a novel hierarchical common and specific structure with different order information[J]. 中国化学工程学报, 2021, 39(11): 183-192.

Yun Wang, Yuchen He, De Gu. Quality oriented multimode processes monitoring based on a novel hierarchical common and specific structure with different order information[J]. Chinese Journal of Chemical Engineering, 2021, 39(11): 183-192.

参考文献

[1] K. Peng, K. Zhang, B. You, J. Dong, Quality-related prediction and monitoring of multi-mode processes using multiple PLS with application to an industrial hot strip mill, Neurocomputing 168(2015) 1094-1103.
[2] S. Yin, X.S. Ding, X. Xie, H. Luo, A review on basic data-driven approaches for industrial process monitoring, IEEE Trans. Ind. Electron. 61(11) (2014) 6418- 6428.
[3] Z. Ge, Z. Song, F. Gao, Review of recent research on data-based process monitoring, Ind. Eng. Chem. Res. 52(10) (2013) 3543-3562.
[4] Q. Jiang, X. Yan, B. Huang, Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes, Ind. Eng. Chem. Res. 58(29) (2019) 12899-12912.
[5] J. Zhu, Z. Ge, Z. Song, Non-Gaussian industrial process monitoring with probabilistic independent component analysis, IEEE Trans. Autom. Sci. Eng. 14(2) (2017) 1309-1319.
[6] Z. Ge, J. Chen, Plant-wide industrial process monitoring:A distributed modeling framework, IEEE Trans. Ind. Inf. 12(1) (2016) 310-321.
[7] J. Zhu, Y. Yao, D. Li, F. Gao, Monitoring big process data of industrial plants with multiple operating modes based on Hadoop, J. Taiwan Inst. Chem. Eng. 91(2018) 10-21.
[8] S. Tan, F. Wang, J. Peng, Y. Chang, S. Wang, Multimode process monitoring based on mode identification, Ind. Eng. Chem. Res. 51(1) (2011) 374-388.
[9] L. Yao, Z. Ge, Distributed parallel deep learning of Hierarchical Extreme Learning Machine for multimode quality prediction with big process data, Eng. Appl. Artif. Intell. 81(2019) 450-465.
[10] Y. Qin, C. Zhao, S. Zhang, F. Gao, Multimode and multiphase batch processes understanding and monitoring based on between-mode similarity evaluation and multimode discriminative information analysis, Ind. Eng. Chem. Res. 56(34) (2017) 9679-9690.
[11] Z. Lou, Y. Wang, Multimode continuous processes monitoring based on hidden semi-Markov model and principle component analysis, Ind. Eng. Chem. Res. 56(46) (2017) 13800-13811.
[12] S. Zhang, C. Zhao, F. Gao, Two-directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase batch process monitoring with uneven lengths, Chem. Eng. Sci. 178(2018) 104-117.
[13] L. Li, X. Yuan, Y. Wang, B. Sun, D. Wu, A two-layer fuzzy synthetic strategy for operational performance assessment of an industrial hydrocracking process, Control Eng. Pract. 93(2019) 104187.
[14] Y. Zhang, T. Chai, Z. Li, C. Yang, Modeling and monitoring of dynamic processes, IEEE Trans. Neural Networks Learn. Syst. 23(2) (2012) 277-284.
[15] S.J. Qin, Statistical process monitoring:basics and beyond, J. Chemom. 17(8-9) (2010) 480-502.
[16] G. Li, C.F. Alcala, S.J. Qin, D. Zhou, Generalized reconstruction-based contributions for output-relevant fault diagnosis with application to the tennessee eastman process, IEEE Trans. Control Syst. Technol. 19(5) (2011) 1114-1127.
[17] C. Zhao, F. Gao, D. Niu, F. Wang, A two-step basis vector extraction strategy for multiset variable correlation analysis, Chemometr. Intell. Lab. Syst. 107(1) (2011) 147-154.
[18] C. Zhao, Y. Yao, F. Gao, F. Wang, Statistical analysis and online monitoring for multimode processes with between-mode transitions, Chem. Eng. Sci. 65(22) (2010) 5961-5975.
[19] C. Zhao, W. Wang, Y. Qin, F. Gao, Comprehensive subspace decomposition with analysis of between-mode relative changes for multimode process monitoring, Ind. Eng. Chem. Res. 54(12) (2015) 3154-3166.
[20] Y. Zhang, C. Wang, R. Lu, Modeling and monitoring of multimode process based on subspace separation, Chem. Eng. Res. Des. 91(5) (2013) 831-842.
[21] Y. Zhang, Y. Fan, N. Yang, Fault diagnosis of multimode processes based on similarities, IEEE Trans. Ind. Electron. 63(4) (2016) 2606-2614.
[22] Y. Zhang, J. An, C. Ma, Fault detection of non-Gaussian processes based on model migration, IEEE Trans. Control Syst. Technol. 21(5) (2013) 1517-1526.
[23] Y. Zhang, S. Li, Modeling and monitoring between-mode transition of multimodes processes, IEEE Trans. Ind. Inf. 9(4) (2013) 2248-2255.
[24] C. Zhao, B. Huang, A full-condition monitoring method for nonstationary dynamic chemical processes with cointegration and slow feature analysis, AIChE J. 64(5) (2018) 1662-1681.
[25] K. Zhang, K. Peng, J. Dong, A common and individual feature extraction-based multimode process monitoring method with application to the finishing mill process, IEEE Trans. Ind. Inf. 14(11) (2018) 4841-4850.
[26] Q. Jiang, B. Wang, X. Yan, Fault detection in non-Gaussian processes based on mutual information weighted independent component analysis, J. Chem. Eng. Jpn. 47(1) (2014) 60-68.
[27] J. Zeng, L. Xie, U. Kruger, C. Gao, A non-Gaussian regression algorithm based on mutual information maximization, Chemometr. Intell. Lab. Syst. 111(1) (2012) 1-19.
[28] Y. He, J. Zeng, Double layer distributed process monitoring based on hierarchical multi-block decomposition, IEEE Access 7(2019) 17337-17346.
[29] Y. He, B. Zhu, C. Liu, J. Zeng, Quality-related locally weighted non-Gaussian regression based soft sensing for multimode processes, Ind. Eng. Chem. Res. 57(51) (2018) 17452-17461.
[30] E. Russell, L. Chiang, R. Braatz, Data-driven methods for fault detection and diagnosis in chemical processes, Springer, London, 2000, pp. 1-192.
[31] Y. He, L. Zhou, Z. Ge, Z. Song, Distributed model projection based transition processes recognition and quality-related fault detection, Chemometr. Intell. Lab. Syst. 159(2016) 69-79.

Quality oriented multimode processes monitoring based on a novel hierarchical common and specific structure with different order information

Quality oriented multimode processes monitoring based on a novel hierarchical common and specific structure with different order information

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价