Intelligent Process Fault Diagnosis for Nonlinear Systems with Uncertain Plant Model via Extended State Observer and Soft Computing
Affiliation(s)
Engineering Dean’s Office, Cleveland State University, Cleveland, USA. Mechanical & Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou, China. Electrical and Computer Engineering, Cleveland State University, Cleveland, USA. Electrical and Computer Engineering, Gannon University, Erie, USA.
Engineering Dean’s Office, Cleveland State University, Cleveland, USA. Mechanical & Electronic Engineering, Fujian Agriculture and Forestry University, Fuzhou, China. Electrical and Computer Engineering, Cleveland State University, Cleveland, USA. Electrical and Computer Engineering, Gannon University, Erie, USA.
ABSTRACT
There have been many studies on observer-based fault detection and isolation (FDI), such as using unknown input observer and generalized observer. Most of them require a nominal mathematical model of the system. Unlike sensor faults, actuator faults and process faults greatly affect the system dynamics. This paper presents a new process fault diagnosis technique without exact knowledge of the plant model via Extended State Observer (ESO) and soft computing. The ESO’s augmented or extended state is used to compute the system dynamics in real time, thereby provides foundation for real-time process fault detection. Based on the input and output data, the ESO identifies the un-modeled or incorrectly modeled dynamics combined with unknown external disturbances in real time and provides vital information for detecting faults with only partial information of the plant, which cannot be easily accomplished with any existing methods. Another advantage of the ESO is its simplicity in tuning only a single parameter. Without the knowledge of the exact plant model, fuzzy inference was developed to isolate faults. A strongly coupled three-tank nonlinear dynamic system was chosen as a case study. In a typical dynamic system, a process fault such as pipe blockage is likely incipient, which requires degree of fault identification at all time. Neural networks were trained to identify faults and also instantly determine degree of fault. The simulation results indicate that the proposed FDI technique effectively detected and isolated faults and also accurately determine the degree of fault. Soft computing (i.e. fuzzy logic and neural networks) makes fault diagnosis intelligent and fast because it provides intuitive logic to the system and real-time input-output mapping.
There have been many studies on observer-based fault detection and isolation (FDI), such as using unknown input observer and generalized observer. Most of them require a nominal mathematical model of the system. Unlike sensor faults, actuator faults and process faults greatly affect the system dynamics. This paper presents a new process fault diagnosis technique without exact knowledge of the plant model via Extended State Observer (ESO) and soft computing. The ESO’s augmented or extended state is used to compute the system dynamics in real time, thereby provides foundation for real-time process fault detection. Based on the input and output data, the ESO identifies the un-modeled or incorrectly modeled dynamics combined with unknown external disturbances in real time and provides vital information for detecting faults with only partial information of the plant, which cannot be easily accomplished with any existing methods. Another advantage of the ESO is its simplicity in tuning only a single parameter. Without the knowledge of the exact plant model, fuzzy inference was developed to isolate faults. A strongly coupled three-tank nonlinear dynamic system was chosen as a case study. In a typical dynamic system, a process fault such as pipe blockage is likely incipient, which requires degree of fault identification at all time. Neural networks were trained to identify faults and also instantly determine degree of fault. The simulation results indicate that the proposed FDI technique effectively detected and isolated faults and also accurately determine the degree of fault. Soft computing (i.e. fuzzy logic and neural networks) makes fault diagnosis intelligent and fast because it provides intuitive logic to the system and real-time input-output mapping.
Cite this paper
P. Lin, D. Ye, Z. Gao and Q. Zheng, "Intelligent Process Fault Diagnosis for Nonlinear Systems with Uncertain Plant Model via Extended State Observer and Soft Computing," Intelligent Control and Automation, Vol. 3 No. 4, 2012, pp. 346-355. doi: 10.4236/ica.2012.34040.
P. Lin, D. Ye, Z. Gao and Q. Zheng, "Intelligent Process Fault Diagnosis for Nonlinear Systems with Uncertain Plant Model via Extended State Observer and Soft Computing," Intelligent Control and Automation, Vol. 3 No. 4, 2012, pp. 346-355. doi: 10.4236/ica.2012.34040.
References
[1] P. M. Frank, S. X. Ding and T. Marcu, “Model-Based Fault Diagnosis in Technical Processes,” Transaction of Institute of Measurement and Control, Vol. 22, No. 1, 2000, pp. 57-101. [2] V. Venkatasubramanian, R. Rengaswamy, K. Yin and S. N. Kavuri, “A Review of Process Fault Detection and Diagnosis: Part I. Quantitative Model-Based Methods,” Computer and Chemical Engineering, Vol. 27, No. 3, 2003, pp. 293-311. doi:10.1016/S0098-1354(02)00160-6 [3] P. P. Lin and X. Li, “Fault Diagnosis, Prognosis and SelfReconfiguration for Nonlinear Dynamic System Using Soft Computing Techniques,” Proceedings of IEEE Conference on Systems, Man and Cybernetics, Taipei, 14-16 December 2006. [4] R. Tarantino, F. Szigeti and E. Colina-Morles, “Generalized Luenberger Observer-Based Fault-Detection Filter Design: An Industrial Application,” Control Engineering Practice, Vol. 8, 2000, pp. 665-671. doi:10.1016/S0967-0661(99)00181-1 [5] S. K. Dash, R. Rengaswamy and V. Venkatasubramanian, “Fault Diagnosis in a Nonlinear CSTR Using Observers,” Annual AIChE Meeting, Reno, 2001, Paper 282i. [6] A. Z. Sotomayor and D. Odloak, “Observer-Based Fault Diagnosis in Chemical Plants,” Chemical Engineering Journal, Vol. 112, 2005, pp. 93-108. doi:10.1016/j.cej.2005.07.001 [7] P. M. Frank, “Advanced Fault Detection and Isolation Schemes Using Nonlinear and Robust Observers,” 10th IFAC World Congress, Munich, 27-31 July 1987. [8] P. M. Frank, “Online Fault-Detection in Uncertain Nonlinear-Systems Using Diagnostic Observers: A Survey,” International Journal of Systems Science, Vol. 25, No. 12, 1994, pp. 2129-2135. doi:10.1080/00207729408949341 [9] P. M. Frank and X. Ding, “Survey of Robust Residual Generation and Evaluation Methods in Observer-Based Fault Detection Systems,” Journal of Process Control, Vol. 7, No. 6, 1997, pp. 403-424. doi:10.1016/S0959-1524(97)00016-4 [10] R. Isermann, “Model-Based Fault-Detection and Diagnosis—Status and Applications,” Annual Reviews in Control, Vol. 29 2005, pp. 71-85. doi:10.1016/j.arcontrol.2004.12.002 [11] V. F. Filareretov, M. K. Vukobratovic and A. N. Zhirabok, “Observer-Based Fault Diagnosis in Manipulation Robots,” Mechatronics, Vol. 9, 1999, pp. 929-939. doi:10.1016/S0957-4158(99)00017-3 [12] A. Xu and Q. Zhang, “Nonlinear System Fault Diagnosis Based on Adaptive Estimation,” Automatica, Vol. 40, 2004, pp. 1181-1193. doi:10.1016/j.automatica.2004.02.018 [13] M. Fang, Y. Tian and L. Guo, “Fault Diagnosis of Nonlinear System Based on Generalized Observer,” Applied Mathematics and Computation, Vol. 185, 2007, pp. 1131-1137. doi:10.1016/j.amc.2006.07.034 [14] H. B. Wang, J. L. Wang and J. Lam, “Robust Fault Detection Observer Design: Iterative LMI Approaches,” ASME Journal of Dynamic Systems, Measurement, and Control, Vol. 129, 2007, pp. 77-82. doi:10.1115/1.2397155 [15] A. Radke, “On Disturbance Estimation and Its Application on Health Monitoring,” Doctoral Dissertation, Cleveland State University, 2006. [16] J. Han, “A Class of Extended State Observers for Uncertain Systems,” Control and Decision, Vol. 10, No. 1, 1995, pp. 85-88. [17] J. Han, “Nonlinear Design Methods for Control Systems,” Proceeding of the 14th IFAC World Congress, Beijing, 4-9 July 1999. [18] Z. Gao, “Scaling and Parameterization Based Controller Tuning,” Proceedings of American Control Conference, Denver, 4-6 June 2003, pp. 4989-4996. [19] Q. Zheng, L.Q. Gao and Z. Gao, “On Estimation of Plant Dynamics and Disturbance from Input-Output Data in Real Time,” Proceedings of the IEEE Multi-Conference on Systems and Control, Singapore City, 1-3 October 2007, pp. 1167-1172. [20] Z. Gao, “Active Disturbance Rejection Control: A Paradigm Shift in Feedback Control System Design,” Proceedings of American Control Conference, Minneapolis, 14-16 June 2006, pp. 4989-4996. [21] P. P. Lin and H. Singh, “Intelligent Model-Free Diagnosis for Multiple Faults in a Nonlinear Dynamic System,” Proceedings of IEEE/ASME Conference on Advanced Intelligent Mechatronics (AIM), Zurich, 4-7 September 2007. [22] P. Zhang and S. Ding, “A Model-Free Approach to Fault Diagnosis of Continuous-Time Systems Based on Time Domain Data,” International Journal of Automation and Computing, Vol. 4, No. 2, 2007, pp. 189-194. doi:10.1007/s11633-007-0189-y [23] M. Kowal and J. Korbicz, “Fault Detection under Fuzzy Model Uncertainty,” International Journal of Automation and Computing, Vol. 4, No. 2, 2007, pp. 117-124. doi:10.1007/s11633-007-0117-1
[1] P. M. Frank, S. X. Ding and T. Marcu, “Model-Based Fault Diagnosis in Technical Processes,” Transaction of Institute of Measurement and Control, Vol. 22, No. 1, 2000, pp. 57-101. [2] V. Venkatasubramanian, R. Rengaswamy, K. Yin and S. N. Kavuri, “A Review of Process Fault Detection and Diagnosis: Part I. Quantitative Model-Based Methods,” Computer and Chemical Engineering, Vol. 27, No. 3, 2003, pp. 293-311. doi:10.1016/S0098-1354(02)00160-6 [3] P. P. Lin and X. Li, “Fault Diagnosis, Prognosis and SelfReconfiguration for Nonlinear Dynamic System Using Soft Computing Techniques,” Proceedings of IEEE Conference on Systems, Man and Cybernetics, Taipei, 14-16 December 2006. [4] R. Tarantino, F. Szigeti and E. Colina-Morles, “Generalized Luenberger Observer-Based Fault-Detection Filter Design: An Industrial Application,” Control Engineering Practice, Vol. 8, 2000, pp. 665-671. doi:10.1016/S0967-0661(99)00181-1 [5] S. K. Dash, R. Rengaswamy and V. Venkatasubramanian, “Fault Diagnosis in a Nonlinear CSTR Using Observers,” Annual AIChE Meeting, Reno, 2001, Paper 282i. [6] A. Z. Sotomayor and D. Odloak, “Observer-Based Fault Diagnosis in Chemical Plants,” Chemical Engineering Journal, Vol. 112, 2005, pp. 93-108. doi:10.1016/j.cej.2005.07.001 [7] P. M. Frank, “Advanced Fault Detection and Isolation Schemes Using Nonlinear and Robust Observers,” 10th IFAC World Congress, Munich, 27-31 July 1987. [8] P. M. Frank, “Online Fault-Detection in Uncertain Nonlinear-Systems Using Diagnostic Observers: A Survey,” International Journal of Systems Science, Vol. 25, No. 12, 1994, pp. 2129-2135. doi:10.1080/00207729408949341 [9] P. M. Frank and X. Ding, “Survey of Robust Residual Generation and Evaluation Methods in Observer-Based Fault Detection Systems,” Journal of Process Control, Vol. 7, No. 6, 1997, pp. 403-424. doi:10.1016/S0959-1524(97)00016-4 [10] R. Isermann, “Model-Based Fault-Detection and Diagnosis—Status and Applications,” Annual Reviews in Control, Vol. 29 2005, pp. 71-85. doi:10.1016/j.arcontrol.2004.12.002 [11] V. F. Filareretov, M. K. Vukobratovic and A. N. Zhirabok, “Observer-Based Fault Diagnosis in Manipulation Robots,” Mechatronics, Vol. 9, 1999, pp. 929-939. doi:10.1016/S0957-4158(99)00017-3 [12] A. Xu and Q. Zhang, “Nonlinear System Fault Diagnosis Based on Adaptive Estimation,” Automatica, Vol. 40, 2004, pp. 1181-1193. doi:10.1016/j.automatica.2004.02.018 [13] M. Fang, Y. Tian and L. Guo, “Fault Diagnosis of Nonlinear System Based on Generalized Observer,” Applied Mathematics and Computation, Vol. 185, 2007, pp. 1131-1137. doi:10.1016/j.amc.2006.07.034 [14] H. B. Wang, J. L. Wang and J. Lam, “Robust Fault Detection Observer Design: Iterative LMI Approaches,” ASME Journal of Dynamic Systems, Measurement, and Control, Vol. 129, 2007, pp. 77-82. doi:10.1115/1.2397155 [15] A. Radke, “On Disturbance Estimation and Its Application on Health Monitoring,” Doctoral Dissertation, Cleveland State University, 2006. [16] J. Han, “A Class of Extended State Observers for Uncertain Systems,” Control and Decision, Vol. 10, No. 1, 1995, pp. 85-88. [17] J. Han, “Nonlinear Design Methods for Control Systems,” Proceeding of the 14th IFAC World Congress, Beijing, 4-9 July 1999. [18] Z. Gao, “Scaling and Parameterization Based Controller Tuning,” Proceedings of American Control Conference, Denver, 4-6 June 2003, pp. 4989-4996. [19] Q. Zheng, L.Q. Gao and Z. Gao, “On Estimation of Plant Dynamics and Disturbance from Input-Output Data in Real Time,” Proceedings of the IEEE Multi-Conference on Systems and Control, Singapore City, 1-3 October 2007, pp. 1167-1172. [20] Z. Gao, “Active Disturbance Rejection Control: A Paradigm Shift in Feedback Control System Design,” Proceedings of American Control Conference, Minneapolis, 14-16 June 2006, pp. 4989-4996. [21] P. P. Lin and H. Singh, “Intelligent Model-Free Diagnosis for Multiple Faults in a Nonlinear Dynamic System,” Proceedings of IEEE/ASME Conference on Advanced Intelligent Mechatronics (AIM), Zurich, 4-7 September 2007. [22] P. Zhang and S. Ding, “A Model-Free Approach to Fault Diagnosis of Continuous-Time Systems Based on Time Domain Data,” International Journal of Automation and Computing, Vol. 4, No. 2, 2007, pp. 189-194. doi:10.1007/s11633-007-0189-y [23] M. Kowal and J. Korbicz, “Fault Detection under Fuzzy Model Uncertainty,” International Journal of Automation and Computing, Vol. 4, No. 2, 2007, pp. 117-124. doi:10.1007/s11633-007-0117-1