An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

doi:10.1016/j.cjche.2015.01.014

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (8): 1357-1363.DOI: 10.1016/j.cjche.2015.01.014

• 过程系统工程与过程安全 • 上一篇下一篇

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

Yawei Yang, Yuxin Ma, Bing Song, Hongbo Shi

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

收稿日期:2014-09-03 修回日期:2015-01-25 出版日期:2015-08-28 发布日期:2015-09-26
通讯作者: Hongbo Shi
基金资助:
Supported by the National Natural Science Foundation of China (61374140) and Shanghai Pujiang Program (12PJ1402200).

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

Yawei Yang, Yuxin Ma, Bing Song, Hongbo Shi

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2014-09-03 Revised:2015-01-25 Online:2015-08-28 Published:2015-09-26
Contact: Hongbo Shi
Supported by:
Supported by the National Natural Science Foundation of China (61374140) and Shanghai Pujiang Program (12PJ1402200).

摘要/Abstract

摘要： A novel approach named aligned mixture probabilistic principal component analysis (AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations, the AMPPCA algorithm first estimates a statistical description for each operating mode by applyingmixture probabilistic principal component analysis (MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, bothwithin-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

关键词: Multimode process monitoring, Mixture probabilistic principal component, analysis, Model alignment, Fault detection

Abstract: A novel approach named aligned mixture probabilistic principal component analysis (AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations, the AMPPCA algorithm first estimates a statistical description for each operating mode by applyingmixture probabilistic principal component analysis (MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, bothwithin-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

Key words: Multimode process monitoring, Mixture probabilistic principal component, analysis, Model alignment, Fault detection

Yawei Yang, Yuxin Ma, Bing Song, Hongbo Shi. An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes[J]. Chinese Journal of Chemical Engineering, 2015, 23(8): 1357-1363.

Yawei Yang, Yuxin Ma, Bing Song, Hongbo Shi. An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes[J]. Chin.J.Chem.Eng., 2015, 23(8): 1357-1363.

参考文献

[1] U. Kruger, L. Xie, Advances in Statistical Monitoring of Complex Multivariate Processes:With Applications in Industrial Process Control, Wiley Press, Chichester, England, 2012.

[2] S.J. Zhao, J. Zhang, Y.M. Xu, Performance monitoring of processes with multiple operating modes through multiple PLS models, J. Process Control 16(2006) 763-772.

[3] Z.B. Zhu, Z.H. Song, A. Palazoglu, Process pattern construction and multi-mode monitoring, J. Process Control 22(2012) 247-262.

[4] C.H. Zhao, Concurrent phase partition and between-mode statistical analysis for multimode and multiphase batch process monitoring, AICHE J. 60(2014) 559-573.

[5] S. Natarajan, R. Srinivasan, Multi-model based process condition monitoring of offshore oil and gas production process, Chem. Eng. Res. Des. 88(2010) 572-591.

[6] S.J. Zhao, J. Zhang, Y.M. Xu, Monitoring of processes with multiple operating modes through multiple principal component analysis models, Ind. Eng. Chem. Res. 43(2004) 7025-7035.

[7] T. Feital, U. Kruger, J. Dutra, J.C. Pinto, E.L. Lima, Modeling and performance monitoring of multivariate multimodal processes, AICHE J. 59(2013) 1557-1569.

[8] J. Yu, S.J. Qin, Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models, AICHE J. 54(2008) 1811-1829.

[9] X. Xie, H.B. Shi, Multimode process monitoring based on Fuzzy C-means in locality preserving projection subspace, Chin. J. Chem. Eng. 20(2012) 1174-1179.

[10] Z.Q. Ge, Z.H. Song, Mixture Bayesian regularization method of PPCA for multimode process monitoring, AICHE J. 56(2010) 2838-2849.

[11] Z.Q. Ge, Z.H. Song, Maximum-likelihood mixture factor analysismodel and its application for process monitoring, Chemom. Intell. Lab. Syst. 102(2010) 53-61.

[12] H.D. Jin, Y.H. Lee, G. Lee, C.H. Han, Robust recursive principal component analysis modeling for adaptive monitoring, Ind. Eng. Chem. Res. 45(2006) 696-703.

[13] Z.Q. Ge, Z.H. Song, Online monitoring of nonlinear multiple mode processes based on adaptive local model approach, Control. Eng. Pract. 16(2008) 1427-1437.

[14] X. Xie, H.B. Shi, Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models, Ind. Eng. Chem. Res. 51(2012) 5497-5505.

[15] J. Yu, A particle filter driven dynamic Gaussianmixture model approach for complex process monitoring and fault diagnosis, J. Process Control 22(2012) 778-788.

[16] Y.X. Ma, H.B. Shi, H.H. Ma, M.L. Wang, Dynamic process monitoring using adaptive local outlier factor, Chemom. Intell. Lab. Syst. 127(2013) 89-101.

[17] H.H. Ma, Y. Hu, H.B. Shi, Fault detection and identification based on the neighborhood standardized local outlier factor method, Ind. Eng. Chem. Res. 52(2013) 2389-2402.

[18] K. Ghosh, R. Srinivasan, Immune system inspired approach to process monitoring and fault diagnosis, Ind. Eng. Chem. Res. 50(2011) 1637-1651.

[19] Y.W. Zhang, C.Wang, R.Q. Lu,Modeling andmonitoring ofmultimode process based on subspace separation, Chem. Eng. Res. Des. 91(2013) 831-842.

[20] Y.W. Zhang, S. Li, Modeling and monitoring of nonlinear multi-mode processes, Control. Eng. Pract. 22(2014) 194-204.

[21] Y.W. Teh, S. Roweis, Automatic alignment of local representations, Adv. Neural Inf. Process. Syst. 15(2002) 841-848.

[22] S. Yoon, J.F. MacGregor, Fault diagnosis with multivariate statistical models part I:using steady state fault signatures, J. Process Control 11(2001) 387-400.

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	Sinu Poolachira, Sivasubramanian Velmurugan. Graphene oxide/hydrotalcite modified polyethersulfone nanohybrid membrane for the treatment of lead ion from battery industrial effluent[J]. 中国化学工程学报, 2023, 60(8): 253-261.
[2]	Chaobo Zhang, Xiaoyong Yang, Jian Dai, Wenxia Liu, Hang Yang, Zhishan Bai. Efficient extraction of phenol from wastewater by ionic micro-emulsion method: Anionic and cationic[J]. 中国化学工程学报, 2023, 58(6): 137-145.
[3]	Chenyang Zhao, Yinhan Cheng, Guangfei Qu, Yongheng Yuan, Fenghui Wu, Ye Liu, Shan Liu, Junyan Li, Ping Ning. High-performance liquid-phase catalytic purification of phosphine in tail gas using Pd(II)/Cu(II) composite[J]. 中国化学工程学报, 2023, 57(5): 98-108.
[4]	Xiongzhuo Zhu, Dali Gao, Chong Yang, Chunjie Yang. A blast furnace fault monitoring algorithm with low false alarm rate: Ensemble of greedy dynamic principal component analysis-Gaussian mixture model[J]. 中国化学工程学报, 2023, 57(5): 151-161.
[5]	Fangyuan Ma, Cheng Ji, Jingde Wang, Wei Sun. Early identification of process deviation based on convolutional neural network[J]. 中国化学工程学报, 2023, 56(4): 104-118.
[6]	Yiming Bai, Shuaiyu Xiang, Feifan Cheng, Jinsong Zhao. A dynamic-inner LSTM prediction method for key alarm variables forecasting in chemical process[J]. 中国化学工程学报, 2023, 55(3): 266-276.
[7]	Dayang Wang, Ningde Jin, Lusheng Zhai, Yingyu Ren. Quantitative research of the liquid film characteristics in upward vertical gas, oil and water flows[J]. 中国化学工程学报, 2023, 54(2): 67-79.
[8]	P.M. Patil, Bharath Goudar. Entropy generation analysis from the time-dependent quadratic combined convective flow with multiple diffusions and nonlinear thermal radiation[J]. 中国化学工程学报, 2023, 53(1): 46-55.
[9]	Jie Jiang, Wei Cheng, Qiuyu Tang, Xun Pan, Jinjin Li, Ling Zhao, Zhenhao Xi, Weikang Yuan. Multiblock poly(ether-b-amide) copolymers comprised of PA1212 and PPO-PEO-PPO with specific moisture-responsive and antistatic properties[J]. 中国化学工程学报, 2023, 53(1): 421-430.
[10]	Jiaxin Zhang, Wenjia Luo, Yiyang Dai, Yuman Yao. Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes[J]. 中国化学工程学报, 2022, 47(7): 54-70.
[11]	Wenjuan Bai, Dianming Chu, Kuanxin Tang, Lei Geng, Yan Li, Yan He. The motion mechanism and characteristic of bubble in a pseudo-2D tapered fluidized bed[J]. 中国化学工程学报, 2022, 46(6): 255-270.
[12]	Yingjie Zhou, Wenhui Zhang, Shengwei Yu, Haibo Jiang, Chunzhong Li. Patterned catalyst layer boosts the performance of proton exchange membrane fuel cells by optimizing water management[J]. 中国化学工程学报, 2022, 44(4): 246-252.
[13]	Yuanjie Li, Qiuxiang Yin, Meijing Zhang, Ying Bao, Baohong Hou, Jingkang Wang, Jiting Huang, Ling Zhou. Characterization and structure analysis of the heterosolvate of erythromycin thiocyanate[J]. 中国化学工程学报, 2022, 44(4): 268-274.
[14]	Jason Williams, Hussameldin Ibrahim, Nima Karimi, Kelvin Tsun Wai Ng. Heterogeneous numerical modelling for the auto thermal reforming of crude glycerol in a fixed bed reactor[J]. 中国化学工程学报, 2022, 42(2): 261-268.
[15]	Wenhui Yang, Haoyu Yin, Zhihong Yuan, Bingzhen Chen. Flexibility analysis for continuous ibuprofen manufacturing processes[J]. 中国化学工程学报, 2022, 51(11): 115-125.