Fault detection and isolation in hybrid process systems using a combined data-driven and observer-design methodology

AIChE Journal

Volumn 60, Issue 8, 2014, Pages 2805-2814

  Tong, Chudong ^a,b   El Farra, Nael H ^b   Palazoglu, Ahmet ^b   Yan, Xuefeng ^a  

^a School of Information Science and Engineering   (China)

^b University of California Davis   (United States)

Author keywords

Data driven techniques; Fault detection and isolation; Hybrid process systems; Unknown input observer

Indexed keywords

FAULT TOLERANT COMPUTER SYSTEMS; HYBRID SYSTEMS;

CONTINUOUS STIRRED TANKS; DATA-DRIVEN TECHNIQUES; FAULT DETECTION AND ISOLATION; GAUSSIAN MIXTURE MODEL; HYBRID PROCESS; MULTI VARIATE ANALYSIS; SUBSPACE MODEL IDENTIFICATIONS; UNKNOWN INPUT OBSERVER;

FAULT DETECTION;

EID: 84903793852     PISSN: 00011541     EISSN: 15475905     Source Type: Journal    
DOI: 10.1002/aic.14475     Document Type: Article

References (45)

1. Qin SJ. Statistical process monitoring: basics and beyond. J Chemom. 2003;17:480-502.
2. Veenkatasubramanian V, Rengaswamy R, Yin K, Kavuri SN. A review of process fault detection and diagnosis: part I: quantitative model-based methods. Comput Chem Eng. 2003;27:293-311.
3. Simani S, Fantuzzi C, Patton RJ. Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques. London: Spring, 2003.
4. Xie L, Kruger U, Lieftucht D, Littler T, Chen Q, Wang SQ. Statistical monitoring of dynamic multivariate process part 1: modeling autocorrelation and cross-correlation. Ind Eng Chem Res. 2006;45:1659-1676.
5. Yoon S, MacGregor JF. Statistical and causal model-based approaches to fault detection and isolation. AIChE J. 2000;46:1813-1824.
6. Kruger U, Xie L. Statistical Monitoring of Complex Multivariate Processes. Chichester: Wiley, 2012.
7. MacGregor JF, Yu H, Nunoz SG, Flores-Cerrillo J. Data-based latent variable methods for process analysis, monitoring and control. Comput Chem Eng. 2005;29:1217-1223.
8. Ding SX. Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools. Berlin: Springer, 2008.
9. Patton RJ, Chen J. Observer-based fault detection and isolation: robustness and applications. Control Eng Pract. 1997;5:671-682.
10. Sotomayor OAZ, Odloak D. Observer-based fault diagnosis in chemical plants. Chem Eng J. 2005;112:93-108.
11. Chang S, You W, Hsu P. Design of general structured observers for linear systems with unknown inputs. J Franklin Inst. 1997;334:213-232.
12. Chen W, Saif M. Fault detection and isolation based on novel unknown input observer design. In: Proceedings of the American Control Conference, Minneapolis, Minnesota, 2006.
13. Xiong Y, Saif M. Unknown disturbance inputs estimation based on a state functional observer design. Automatica. 2003;39:1389-1398.
14. Gao Z, Ding SX. Sensor fault reconstruction and sensor compensation for a class of nonlinear state-space systems via a descriptor system approach. IET Control Theory Appl. 2007;1:578-585.
15. Schubert U, Kruger U, Arellano-Garcia H, Feital TDS, Wozny G. Unified model-based fault diagnosis for three industrial application studies. Control Eng Pract. 2011;19:479-490.
16. Chiang L, Russell E, Braatz R. Fault Detection and Diagnosis in Industrial Systems. London, Great Britain: Springer-Verlag, 2001.
17. Favoreel W, Moor BD, Overschee PV. Subspace state space system identification for industrial processes. J Process Control. 2000;10:149-155.
18. Yu J, Qin SJ. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AIChE J. 2008;54:1811-1829.
19. Qin SJ. Survey on data-driven industrial process monitoring and diagnosis. Annu Rev Control. 2012;36:220-234.
20. Ding SX, Zhang P, Naik A, Ding EL, Huang B. Subspace method aided data-driven design of fault detection and isolation systems. J Process Control. 2009;19:1496-1510.
21. Schubert U, Kruger U, Wozny G, Arellano-Garcia H. Input reconstruction for statistical-based fault detection and isolation. AIChE J. 2012;58:1513-1523.
22. Ohran BJ, Peña DM, Davis JF, Christofides PD. Enhancing data-based fault isolation through nonlinear control. AIChE J. 2008;54:223-241.
23. Ohran BJ, Liu J, Peña DM, Christofides PD, Davis JF. Data-based fault detection and isolation using feedback control: output feedback and optimality. Chem Eng Sci. 2009;64:2370-2383.
24. Hu Y, El-Farra NH. Robust fault detection and monitoring of hybrid process systems with uncertain mode transitions. AIChE J. 2011;57:2783-2794.
25. Zhao SJ, Zhang J, Xu YM. Monitoring of processes with multiple operating modes through multiple principal component analysis models. Ind Eng Chem Res. 2004;43:7025-7035.
26. Feital T, Kruger U. Dutra J. Pinto JC, Lima EL. Modeling and performance monitoring of multivariate multimodal processes. AIChE J. 2013;59:1557-1569.
27. Duda RO, Hart PE, Stork DG. Pattern Classification, 2nd ed. New York: Wiley, 2001.
28. Figueiredo MAF, Jain AK. Unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell. 2002;24:381-396.
29. Hu W, Choi KS, Gu Y, Wang S. Minimum-maximum local structure information for feature selection. Pattern Recognit Lett. 2013;34:527-535.
30. Jiménez-Rodríguez LO, Arzuaga-Cruz E, Vélez-Reyes M. Unsupervised linear feature-extraction methods and their effects in the classification of high-dimensional data. IEEE Trans Geosci Remote Sens. 2007;45:469-483.
31. Cai D, He X, Han J. Document clustering using locality preserving indexing. IEEE Trans Knowl Data Eng. 2005;17:1624-1637.
32. Qin SJ. An overview of subspace identification. Comput Chem Eng. 2006;30:1502-1513.
33. Overschee PV. Subspace identification for linear systems, 2002. Available at: http://www.mathworks.com/matlabcentral/. Accessed on April 2013.
34. Saif M, Guan Y. A new approach to robust fault detection and identification, IEEE Trans Aerosp Electron Syst. 1993;29:685-695.
35. El-Farra NH, Ghantasala S. Actuator fault isolation and reconfiguration in transport-reaction processes. AIChE J. 2007;53:1518-1537.
36. Ghantasala S, El-Farra NH. Robust actuator fault isolation and management in constrained uncertain parabolic PDE systems. Automatica. 2009;45:2386-2373.
37. Giridhar A, El-Farra NH. A unified framework for detection, isolation and compensation of actuator faults in uncertain particulate processes. Chem Eng Sci. 2009;64:2963-2977.
38. Kourti T. Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. J Chemom. 2003;17:93-109.
39. Zhu Z, Song Z, Palazoglu A. Transition process modeling and monitoring based on dynamic ensemble clustering and multiclass support vector data description. Ind Eng Chem Res. 2011;50:13969-13983.
40. Thornhill NF, Patwardhan SC, Shah SL. A continuous stirred tank heater simulation model with applications. J Process Control. 2008;18:347-360.
41. Thornhill NF, Patwardhan SC, Shah SL. The CSTH simulation, 2007. Available at: http://personal-pages.ps.ic.ac.uk/∼nina/CSTHSimulation/index.htm. Accessed on April, 2013.
42. Downs JJ, Vogel EF. A plant-wide industrial process control problem. Comput Chem Eng. 1993;17:245-255.
43. Zhu ZB, Song ZH, Palazoglu A. Process pattern construction and multi-mode monitoring. J Process Control. 2012;22:247-262.
44. Ricker NL. Decentralized control of Tennessee Eastman challenge process. J Process Control. 1996;6:205-221.
45. Ricker NL. Tennessee Eastman challenge archive, 2002. Available at: http://depts.washington.edu/control/LARRY/TE/download.html. Accessed on Sept. 2012.