Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

doi:10.1016/j.cjche.2020.08.035

中国化学工程学报 ›› 2021, Vol. 29 ›› Issue (1): 253-265.DOI: 10.1016/j.cjche.2020.08.035

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

Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

Muhammad Nawaz¹, Abdulhalim Shah Maulud^1,2, Haslinda Zabiri¹, Syed Ali Ammar Taqvi³, Alamin Idris⁴

1 Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia;
2 Centre of Contaminant Control&Utilization(CenCoU), Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia;
3 Department of Chemical Engineering, NED University of Engineering&Technology, Karachi 75270, Pakistan;
4 Department of Chemical and Engineering Sciences, Karlstad University, Karlstad, Sweden

收稿日期:2020-06-03 修回日期:2020-07-30 出版日期:2021-01-28 发布日期:2021-04-02
通讯作者: Abdulhalim Shah Maulud
基金资助:
The authors would like to acknowledge the Universiti Teknologi PETRONAS (UTP), Chemical Engineering Department for the technical and administrative support and the financial support from the Yayasan UTP grant (Cost centre: 015LC0-132).

Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

Muhammad Nawaz¹, Abdulhalim Shah Maulud^1,2, Haslinda Zabiri¹, Syed Ali Ammar Taqvi³, Alamin Idris⁴

1 Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia;
2 Centre of Contaminant Control&Utilization(CenCoU), Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia;
3 Department of Chemical Engineering, NED University of Engineering&Technology, Karachi 75270, Pakistan;
4 Department of Chemical and Engineering Sciences, Karlstad University, Karlstad, Sweden

Received:2020-06-03 Revised:2020-07-30 Online:2021-01-28 Published:2021-04-02
Contact: Abdulhalim Shah Maulud
Supported by:
The authors would like to acknowledge the Universiti Teknologi PETRONAS (UTP), Chemical Engineering Department for the technical and administrative support and the financial support from the Yayasan UTP grant (Cost centre: 015LC0-132).

摘要/Abstract

摘要： Process monitoring techniques are of paramount importance in the chemical industry to improve both the product quality and plant safety. Small or incipient irregularities may lead to severe degradation in complex chemical processes, and the conventional process monitoring techniques cannot detect these irregularities. In this study to improve the performance of monitoring, an online multiscale fault detection approach is proposed by integrating multiscale principal component analysis (MSPCA) with cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts. The new Hotelling's T² and square prediction error (SPE) based fault detection indices are proposed to detect the incipient irregularities in the process data. The performance of the proposed fault detection methods was tested for simulated data obtained from the CSTR system and compared to that of conventional PCA and MSPCA based methods. The results demonstrate that the proposed EWMA based MSPCA fault detection method was successful in detecting the faults. Moreover, a comparative study shows that the SPE-EWMA monitoring index exhibits a better performance with lower values of missed detections ranging from 0% to 0.80% and false alarms ranging from 0% to 21.20%.

关键词: Chemical process system, CSTR, Fault detection, Multiscale, Principal component analysis, Process monitoring

Abstract: Process monitoring techniques are of paramount importance in the chemical industry to improve both the product quality and plant safety. Small or incipient irregularities may lead to severe degradation in complex chemical processes, and the conventional process monitoring techniques cannot detect these irregularities. In this study to improve the performance of monitoring, an online multiscale fault detection approach is proposed by integrating multiscale principal component analysis (MSPCA) with cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts. The new Hotelling's T² and square prediction error (SPE) based fault detection indices are proposed to detect the incipient irregularities in the process data. The performance of the proposed fault detection methods was tested for simulated data obtained from the CSTR system and compared to that of conventional PCA and MSPCA based methods. The results demonstrate that the proposed EWMA based MSPCA fault detection method was successful in detecting the faults. Moreover, a comparative study shows that the SPE-EWMA monitoring index exhibits a better performance with lower values of missed detections ranging from 0% to 0.80% and false alarms ranging from 0% to 21.20%.

Key words: Chemical process system, CSTR, Fault detection, Multiscale, Principal component analysis, Process monitoring

Muhammad Nawaz, Abdulhalim Shah Maulud, Haslinda Zabiri, Syed Ali Ammar Taqvi, Alamin Idris. Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework[J]. 中国化学工程学报, 2021, 29(1): 253-265.

参考文献

[1] W. Tian, Y. Ren, Y. Dong, S. Wang, L. Bu, Fault monitoring based on mutual information feature engineering modeling in chemical process, Chin. J. Chem. Eng. 27(2019) 2491-2497.
[2] R. Fezai, M. Mansouri, K. Abodayeh, H. Nounou, M. Nounou, Online reduced gaussian process regression based generalized likelihood ratio test for fault detection, J. Process Control 85(2020) 30-40.
[3] S.A. Taqvi, L.D. Tufa, H. Zabiri, A.S. Maulud, F. Uddin, Fault detection in distillation column using NARX neural network, Neural Comput. Appl. 32(2020) 3503-3519.
[4] S. Joe Qin, Statistical process monitoring:basics and beyond, J. Chemom. 17(2003) 480-502.
[5] X.Z. Wang, S. Medasani, F. Marhoon, H. Albazzaz, Multidimensional visualization of principal component scores for process historical data analysis, Ind. Eng. Chem. Res. 43(2004) 7036-7048.
[6] C. Zhao, H. Sun, Dynamic distributed monitoring strategy for large-scale nonstationary processes subject to frequently varying conditions under closed-loop control, IEEE Trans. Ind. Electron. 66(2019) 4749-4758.
[7] A.M. Benkouider, J.C. Buvat, J.M. Cosmao, A. Saboni, Fault detection in semibatch reactor using the EKF and statistical method, J. Loss Prev. Process Ind. 22(2009) 153-161.
[8] S.A. Taqvi, L.D. Tufa, H. Zabiri, A.S. Maulud, F. Uddin, Multiple fault diagnosis in distillation column using multikernel support vector machine, Ind. Eng. Chem. Res. 57(2018) 14689-14706.
[9] S.A. Taqvi, L.D. Tufa, H. Zabiri, S. Mahadzir, A.S. Maulud, F. Uddin, Artificial neural network for anomalies detection in distillation column, Asian Simulat. Conf. (2017) 302-311.
[10] V. Venkatasubramanian, R. Rengaswamy, S.N. Kavuri, A review of process fault detection and diagnosis:Part Ⅱ:Qualitative models and search strategies, Comput. Chem. Eng. 27(2003) 313-326.
[11] V. Venkatasubramanian, R. Rengaswamy, S.N. Kavuri, K. Yin, A review of process fault detection and diagnosis:Part Ⅲ:Process history based methods, Comput. Chem. Eng. 27(2003) 327-346.
[12] V. Venkatasubramanian, R. Rengaswamy, K. Yin, S.N. Kavuri, A review of process fault detection and diagnosis:Part I:Quantitative model-based methods, Comput. Chem. Eng. 27(2003) 293-311.
[13] S.J. Qin, Survey on data-driven industrial process monitoring and diagnosis, Annu. Rev. Control 36(2012) 220-234.
[14] D.C. Montgomery, Introduction to Statistical Quality Control, John Wiley, Hoboken, N.J., 2005.
[15] M. Staroswiecki, Redondance analytique, Automatique et statistiques pour le diagnostic (2001) 43-68.
[16] J. Gertler, Fault Detection and Diagnosis in Engineering Systems, CRC Press, 1998.
[17] F. Harrou, Y. Sun, S. Khadraoui, Amalgamation of anomaly-detection indices for enhanced process monitoring, J. Loss Prev. Process Ind. 40(2016) 365-377.
[18] Q. Jiang, B. Huang, Distributed monitoring for large-scale processes based on multivariate statistical analysis and Bayesian method, J. Process Control 46(2016) 75-83.
[19] M. Kano, K. Nagao, S. Hasebe, I. Hashimoto, H. Ohno, R. Strauss, et al., Comparison of multivariate statistical process monitoring methods with applications to the Eastman challenge problem, Comput. Chem. Eng. 26(2002) 161-174.
[20] E. Naderi, K. Khorasani, A data-driven approach to actuator and sensor fault detection, isolation and estimation in discrete-time linear systems, Automatica 85(2017) 165-178.
[21] T. Ait-Izem, M.F. Harkat, M. Djeghaba, F. Kratz, On the application of interval PCA to process monitoring:A robust strategy for sensor FDI with new efficient control statistics, J. Process Control 63(2018) 29-46.
[22] N. Basha, M. Nounou, H. Nounou, Multivariate fault detection and classification using interval principal component analysis, J. Comput. Sci. 27(2018) 1-9.
[23] X. Gao, J. Hou, An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process, Neurocomputing 174(2016) 906-911.
[24] T. Rato, M. Reis, E. Schmitt, M. Hubert, B. De Ketelaere, A systematic comparison of PCA-based statistical process monitoring methods for highdimensional, time-dependent processes, AIChE J. 62(2016) 1478-1493.
[25] B. Mnassri, E.M. El Adel, M. Ouladsine, Generalization and analysis of sufficient conditions for PCA-based fault detectability and isolability, Annu. Rev. Control 37(2013) 154-162.
[26] U. Kruger, G. Dimitriadis, Diagnosis of process faults in chemical systems using a local partial least squares approach, AIChE J. 54(2008) 2581-2596.
[27] G. Li, S.J. Qin, D. Zhou, Geometric properties of partial least squares for process monitoring, Automatica 46(2010) 204-210.
[28] R. Fezai, M. Mansouri, O. Taouali, M.F. Harkat, N. Bouguila, Online reduced kernel principal component analysis for process monitoring, J. Process Control 61(2018) 1-11.
[29] L. Wang, H. Shi, Improved kernel PLS-based fault detection approach for nonlinear chemical processes, Chin. J. Chem. Eng. 22(2014) 657-663.
[30] F. Jia, E.B. Martin, A.J. Morris, Non-linear principal components analysis for process fault detection, Comput. Chem. Eng. 22(1998) S851-S854.
[31] J.-M. Lee, C. Yoo, S.W. Choi, P.A. Vanrolleghem, I.-B. Lee, Nonlinear process monitoring using kernel principal component analysis, Chem. Eng. Sci. 59(2004) 223-234.
[32] Y. Zhang, C. Ma, Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS, Chem. Eng. Sci. 66(2011) 64-72.
[33] M. Jia, F. Chu, F. Wang, W. Wang, On-line batch process monitoring using batch dynamic kernel principal component analysis, Chemomet. Intell. Lab. Syst. 101(2010) 110-122.
[34] W. Li, H.H. Yue, S. Valle-Cervantes, S.J. Qin, Recursive PCA for adaptive process monitoring, J. Process Control 10(2000) 471-486.
[35] J. Chen, K.-C. Liu, On-line batch process monitoring using dynamic PCA and dynamic PLS models, Chem. Eng. Sci. 57(2002) 63-75.
[36] Y. Zhang, S. Li, Z. Hu, Improved multi-scale kernel principal component analysis and its application for fault detection, Chem. Eng. Res. Des. 90(2012) 1271-1280.
[37] M. Misra, H.H. Yue, S.J. Qin, C. Ling, Multivariate process monitoring and fault diagnosis by multi-scale PCA, Comput. Chem. Eng. 26(2002) 1281-1293.
[38] B.R. Bakshi, Multiscale PCA with application to multivariate statistical process monitoring, AIChE J. 44(1998) 1596-1610.
[39] C. Botre, M. Mansouri, M.N. Karim, H. Nounou, M. Nounou, Multiscale PLSbased GLRT for fault detection of chemical processes, J. Loss Prev. Process Ind. 46(2017) 143-153.
[40] M.Z. Sheriff, M. Mansouri, M.N. Karim, H. Nounou, M. Nounou, Fault detection using multiscale PCA-based moving window GLRT, J. Process Control 54(2017) 47-64.
[41] H.B. Aradhye, B.R. Bakshi, R.A. Strauss, J.F. Davis, Multiscale SPC using wavelets:Theoretical analysis and properties, AIChE J. 49(2003) 939-958.
[42] M.S. Reis, P.M. Saraiva, Multiscale statistical process control with multiresolution data, AIChE J. 52(2006) 2107-2119.
[43] A. Azzalini, M. Farge, K. Schneider, Nonlinear wavelet thresholding:A recursive method to determine the optimal denoising threshold, Appl. Comput. Harmon. Anal. 18(2005) 177-185.
[44] M. Mansouri, M.N. Nounou, H.N. Nounou, Multiscale kernel PLS-based exponentially weighted-GLRT and its application to fault detection, IEEE Trans. Emerg. Top. Comput. Intell. 3(2019) 49-58.
[45] A. Maulud, D. Wang, J.A. Romagnoli, A multi-scale orthogonal nonlinear strategy for multi-variate statistical process monitoring, J. Process Control 16(2006) 671-683.
[46] B. Khaldi, F. Harrou, F. Cherif, Y. Sun, Monitoring a robot swarm using a datadriven fault detection approach, Rob. Auton. Syst. 97(2017) 193-203.
[47] M.A. Bin Shams, H.M. Budman, T.A. Duever, Fault detection, identification and diagnosis using CUSUM based PCA, Chem. Eng. Sci. 66(2011) 4488-4498.
[48] C. Botre, M. Mansouri, M. Nounou, H. Nounou, M.N. Karim, Kernel PLS-based GLRT method for fault detection of chemical processes, J. Loss Prev. Process Ind. 43(2016) 212-224.
[49] J. Zhu, Z. Ge, Z. Song, Distributed parallel PCA for modeling and monitoring of large-scale plant-wide processes with big data, IEEE Trans. Ind. Inf. 13(2017) 1877-1885.
[50] J. Josse, F. Husson, Selecting the number of components in principal component analysis using cross-validation approximations, Comput. Stat. Data Anal. 56(2012) 1869-1879.
[51] E. Saccenti, J. Camacho, Determining the number of components in principal components analysis:A comparison of statistical, crossvalidation and approximated methods, Chemomet. Intell. Lab. Syst. 149(2015) 99-116.
[52] M. Zhu, A. Ghodsi, Automatic dimensionality selection from the scree plot via the use of profile likelihood, Comput. Stat. Data Anal. 51(2006) 918-930.
[53] S.G. Mallat, A theory for multiresolution signal decomposition:the wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell. 11(1989) 674-693.
[54] H.N. Nounou, M.N. Nounou, Multiscale fuzzy Kalman filtering, Eng. Appl. Artif. Intell. 19(2006) 439-450.
[55] S. Yoon, J.F. MacGregor, Principal-component analysis of multiscale data for process monitoring and fault diagnosis, AIChE J. 50(2004) 2891-2903.
[56] V.D.C.C. de Vargas, L.F. Dias Lopes, A. Mendonça Souza, Comparative study of the performance of the CuSum and EWMA control charts, Comput. Ind. Eng. 46(2004) 707-724.
[57] S.W. Choi, J. Morris, I.-B. Lee, Nonlinear multiscale modelling for fault detection and identification, Chem. Eng. Sci. 63(2008) 2252-2266.
[58] X. Deng, X. Tian, Sparse kernel locality preserving projection and its application in nonlinear process fault detection, Chin. J. Chem. Eng. 21(2013) 163-170.

Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

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