An Improved Fault Detection Method based on HSMM Application to a Chemical Process
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Published
Mar 4, 2024
Lestari Handayani
VRIGNAT VRIGNAT Frédéric Kratz
https://orcid.org/0000-0003-2668-567X
VRIGNAT VRIGNAT Frédéric Kratz
Abstract
This paper proposes a fault detection method for multivariate statistical process control. The proposed method combines the Forward-Backward Hidden Semi-Markov Model (HSMM) and Principal Component Analysis (PCA). A stochastic automaton was used for multi-mode detection with many observation sequences. We used agglomerative clusters to find the initial parameters of HSMM. We allocated an adaptive threshold and a fixed threshold in each mode for fault detection with PCA, including Hotelling T2 statistic and squared predictive error (Q statistic). We simulated this method on the Tennessee Eastman Process (TEP). Some faults were designed with various runs and times of occurrence. The experimental results were compared with the Mixture Bayesian PCA, Hidden Markov Model (HMM), and HSMM methods. The results are robust with an efficient detection rate. This activity recommends ways to find action plans for multi-mode process monitoring in chemical plants.
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Keywords
'fault detection', 'HSMM', 'Htelling T2, 'T2 Statistic', 'PCA', 'Q statistic', 'Tennessee Eastman Process
References
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Bakdi, A., & Kouadri, A. (2018). An improved plant‐wide fault detection scheme based on PCA and adaptive threshold for reliable process monitoring: Application on the new revised model of Tennessee Eastman process. Journal of Chemometrics, 32(5), e2978. https://doi.org/10.1002/cem.2978
Bathelt, A., Ricker, N. L., & Jelali, M. (2015). Revision of the Tennessee Eastman process model. IFACPapersOnLine, 48(8), 309-314.
Chen, Z., Liu, C., Ding, S. X., Peng, T., Yang, C., Gui, W., & Shardt, Y. A. (2020). A just-in-time-learning-aided canonical correlation analysis method for multimode process monitoring and fault detection. IEEE Transactions on Industrial Electronics, 68(6), 5259-5270.
Downs, J. J., & Vogel, E. F. (1993). A plant-wide industrial process control problem. Computers & chemical engineering, 17(3), 245-255.
Galagedarage Don, M., & Khan, F. (2019). Process fault prognosis using hidden Markov model–bayesian networks hybrid model. Industrial & Engineering Chemistry Research, 58(27), 12041-12053.
Ge, Z., & Song, Z. (2010). Mixture Bayesian regularization method of PPCA for multimode process monitoring. AIChE journal, 56(11), 2838-2849.
Jiang, Q., Yan, X., & Huang, B. (2019). Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes. Industrial & Engineering Chemistry Research, 58(29), 12899-12912.
Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. https://doi.org/10.1098/rsta.2015.0202
Kong, X., & Ge, Z. (2021). Deep learning of latent variable models for industrial process monitoring. IEEE Transactions on Industrial Informatics, 18(10), 6778-6788.
Kulkarni, A., Jayaraman, V. K., & Kulkarni, B. D. (2005). Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process. Computers & chemical engineering, 29(10), 2128-2133.
Liu, T., & Zhu, K. (2020). A switching hidden semi-Markov model for degradation process and its application to time-varying tool wear monitoring. IEEE Transactions on Industrial Informatics, 17(4), 2621-2631.
Lou, Z., & Wang, Y. (2017). Multimode continuous processes monitoring based on hidden semi-Markov model and principal component analysis. Industrial & Engineering Chemistry Research, 56(46), 13800-13811.
Md Nor, N., Che Hassan, C. R., & Hussain, M. A. (2020). A review of data-driven fault detection and diagnosis methods: Applications in chemical process systems. Reviews in Chemical Engineering, 36(4), 513-553.
Nawaz, M., Maulud, A. S., Zabiri, H., Taqvi, S. A. A., & Idris, A. (2021). Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework. Chinese Journal of Chemical Engineering, 29, 253-265.
Nielsen, F. (2016). Hierarchical clustering. In Introduction to HPC with MPI for Data Science (pp. 195-211). Springer.
Odiowei, P.-E. P., & Cao, Y. (2009). Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations. IEEE Transactions on Industrial Informatics, 6(1), 36-45.
Peng, X., Tang, Y., Du, W., & Qian, F. (2017). Multimode process monitoring and fault detection: A sparse modeling and dictionary learning method. IEEE Transactions on Industrial Electronics, 64(6), 4866-4875.
Pratley, A., van Voorthuysen, E., & Chan, R. (2015). A step-by-step approach for modelling complex systems with Partial Least Squares. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 229(5), 847-859.
Quatrini, E., Costantino, F., Di Gravio, G., & Patriarca, R. (2020). Condition-based maintenance—an extensive literature review. Machines, 8(2), 31. https://doi.org/10.3390/machines8020031
Reinartz, C., Kulahci, M., & Ravn, O. (2021). An extended Tennessee Eastman simulation dataset for fault-detection and decision support systems. Computers & chemical engineering, 149, 107281.
Tamssaouet, F., Nguyen, T. K., Medjaher, K., & Orchard, M. E. (2019). Uncertainty quantification in system-level prognostics: application to Tennessee Eastman process. 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT).
Vrignat, P., Kratz, F., & Avila, M. (2022). Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review. Reliability Engineering & System Safety, 218, 108140. https://doi.org/10.1016/j.ress.2021.108140
Wang, F., Tan, S., Yang, Y., & Shi, H. (2016). Hidden Markov model-based fault detection approach for a multimode process. Industrial & Engineering Chemistry Research, 55(16), 4613-4621.
Wu, P., Lou, S., Zhang, X., He, J., & Gao, J. (2020). Novel quality-relevant process monitoring based on dynamic locally linear embedding concurrent canonical correlation analysis. Industrial & Engineering Chemistry Research, 59(49), 21439-21457.
Xia, Y., Wang, W., Song, Z., Xie, Z., Chen, X., & Li, H. (2021). Fault diagnosis of flexible production line machining center based on PCA and ABC-LVQ. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 235(4), 594-604.
Yu, S.-Z., & Kobayashi, H. (2003). An efficient forward-backward algorithm for an explicit-duration hidden Markov model. IEEE signal processing letters, 10(1), 11-14. https://doi.org/10.1109/LSP.2002.806705
Zhang, H., Chen, H., Guo, Y., Wang, J., Li, G., & Shen, L. (2019). Sensor fault detection and diagnosis for a water source heat pump air-conditioning system based on PCA and preprocessed by combined clustering. Applied Thermal Engineering, 160, 114098.
Atamuradov, V., Medjaher, K., Dersin, P., Lamoureux, B., & Zerhouni, N. (2017). Prognostics and health management for maintenance practitioners-Review, implementation and tools evaluation. International Journal of Prognostics and Health Management, 8(3), 1-31.
Bakdi, A., & Kouadri, A. (2018). An improved plant‐wide fault detection scheme based on PCA and adaptive threshold for reliable process monitoring: Application on the new revised model of Tennessee Eastman process. Journal of Chemometrics, 32(5), e2978. https://doi.org/10.1002/cem.2978
Bathelt, A., Ricker, N. L., & Jelali, M. (2015). Revision of the Tennessee Eastman process model. IFACPapersOnLine, 48(8), 309-314.
Chen, Z., Liu, C., Ding, S. X., Peng, T., Yang, C., Gui, W., & Shardt, Y. A. (2020). A just-in-time-learning-aided canonical correlation analysis method for multimode process monitoring and fault detection. IEEE Transactions on Industrial Electronics, 68(6), 5259-5270.
Downs, J. J., & Vogel, E. F. (1993). A plant-wide industrial process control problem. Computers & chemical engineering, 17(3), 245-255.
Galagedarage Don, M., & Khan, F. (2019). Process fault prognosis using hidden Markov model–bayesian networks hybrid model. Industrial & Engineering Chemistry Research, 58(27), 12041-12053.
Ge, Z., & Song, Z. (2010). Mixture Bayesian regularization method of PPCA for multimode process monitoring. AIChE journal, 56(11), 2838-2849.
Jiang, Q., Yan, X., & Huang, B. (2019). Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes. Industrial & Engineering Chemistry Research, 58(29), 12899-12912.
Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. https://doi.org/10.1098/rsta.2015.0202
Kong, X., & Ge, Z. (2021). Deep learning of latent variable models for industrial process monitoring. IEEE Transactions on Industrial Informatics, 18(10), 6778-6788.
Kulkarni, A., Jayaraman, V. K., & Kulkarni, B. D. (2005). Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process. Computers & chemical engineering, 29(10), 2128-2133.
Liu, T., & Zhu, K. (2020). A switching hidden semi-Markov model for degradation process and its application to time-varying tool wear monitoring. IEEE Transactions on Industrial Informatics, 17(4), 2621-2631.
Lou, Z., & Wang, Y. (2017). Multimode continuous processes monitoring based on hidden semi-Markov model and principal component analysis. Industrial & Engineering Chemistry Research, 56(46), 13800-13811.
Md Nor, N., Che Hassan, C. R., & Hussain, M. A. (2020). A review of data-driven fault detection and diagnosis methods: Applications in chemical process systems. Reviews in Chemical Engineering, 36(4), 513-553.
Nawaz, M., Maulud, A. S., Zabiri, H., Taqvi, S. A. A., & Idris, A. (2021). Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework. Chinese Journal of Chemical Engineering, 29, 253-265.
Nielsen, F. (2016). Hierarchical clustering. In Introduction to HPC with MPI for Data Science (pp. 195-211). Springer.
Odiowei, P.-E. P., & Cao, Y. (2009). Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations. IEEE Transactions on Industrial Informatics, 6(1), 36-45.
Peng, X., Tang, Y., Du, W., & Qian, F. (2017). Multimode process monitoring and fault detection: A sparse modeling and dictionary learning method. IEEE Transactions on Industrial Electronics, 64(6), 4866-4875.
Pratley, A., van Voorthuysen, E., & Chan, R. (2015). A step-by-step approach for modelling complex systems with Partial Least Squares. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 229(5), 847-859.
Quatrini, E., Costantino, F., Di Gravio, G., & Patriarca, R. (2020). Condition-based maintenance—an extensive literature review. Machines, 8(2), 31. https://doi.org/10.3390/machines8020031
Reinartz, C., Kulahci, M., & Ravn, O. (2021). An extended Tennessee Eastman simulation dataset for fault-detection and decision support systems. Computers & chemical engineering, 149, 107281.
Tamssaouet, F., Nguyen, T. K., Medjaher, K., & Orchard, M. E. (2019). Uncertainty quantification in system-level prognostics: application to Tennessee Eastman process. 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT).
Vrignat, P., Kratz, F., & Avila, M. (2022). Sustainable manufacturing, maintenance policies, prognostics and health management: A literature review. Reliability Engineering & System Safety, 218, 108140. https://doi.org/10.1016/j.ress.2021.108140
Wang, F., Tan, S., Yang, Y., & Shi, H. (2016). Hidden Markov model-based fault detection approach for a multimode process. Industrial & Engineering Chemistry Research, 55(16), 4613-4621.
Wu, P., Lou, S., Zhang, X., He, J., & Gao, J. (2020). Novel quality-relevant process monitoring based on dynamic locally linear embedding concurrent canonical correlation analysis. Industrial & Engineering Chemistry Research, 59(49), 21439-21457.
Xia, Y., Wang, W., Song, Z., Xie, Z., Chen, X., & Li, H. (2021). Fault diagnosis of flexible production line machining center based on PCA and ABC-LVQ. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 235(4), 594-604.
Yu, S.-Z., & Kobayashi, H. (2003). An efficient forward-backward algorithm for an explicit-duration hidden Markov model. IEEE signal processing letters, 10(1), 11-14. https://doi.org/10.1109/LSP.2002.806705
Zhang, H., Chen, H., Guo, Y., Wang, J., Li, G., & Shen, L. (2019). Sensor fault detection and diagnosis for a water source heat pump air-conditioning system based on PCA and preprocessed by combined clustering. Applied Thermal Engineering, 160, 114098.
Section
Technical Papers