A Review of Fault Diagnosis in Dynamic Control Systems
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Published:
Aug 31, 2018
Keywords:
Fault diagnosis dynamic control system hardware redundancy model-based data and signal
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Abstract
This paper aims to review the techniques for fault diagnosis in dynamic control systems. The basic concept including definition and type of fault is firstly addressed. Following by the current fault diagnosis techniques published in the literatures such as hardware redundancy, model-based, data-driven and signal analysis approach. Discussion and future trend of the fault diagnosis are also presented. This information can be used for improving the fault diagnostic in dynamic control system.
Article Details
How to Cite
[1]
สายใจ เ., “A Review of Fault Diagnosis in Dynamic Control Systems”, sej, vol. 13, no. 2, pp. 153–165, Aug. 2018.
Section
Research Articles
Copyright belongs to Srinakharinwirot University Engineering Journal
References
[1] S. X. Ding, Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithm and Tools. London, Springer-Verlag, 2013.
[2] R. Isermann, Fault Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Heidelberg, Berlin, Springe-Verlag 2006.
[3] L. H Chiang, E. L. Russell and R. D. Braatz, Fault Detection and Diagnosis in Industrial Systems. London, Springer-Verlag, 2001.
[4] P. Weber and S. Gentil, “Fault detection using parameter estimation applied to a winding machine,” In IAR Annual Conference, Duisburg, Germany, 1997, pp. 1-6.
[5] O. Moseler and R. Isermann, “Application of model-based fault detection to a brushless DC motor,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 1015-1020, 2000.
[6] Y. Chetouani, “Change detection in a distillation column based on the generalized likelihood ratio approach,” J. Chem. Eng. & Proc. Tech., vol. 2, no. 5, pp. 1-6, 2011.
[7] A. Hwas and R. Katebi, “Nonlinear observer-based fault detection and isolation for wind turbines,” The 22nd Mediterranean Conference on Control and Automation (MED), Palermo, Italy, 2014, pp. 870-375.
[8] P. Zhang, S. X. Ding and P. Liu, “A lifting Based approach to oObserver based fFault detection of linear periodic systems,” IEEE Trans. Autom. Control, vol. 57, no. 2, pp. 457-462, 2012.
[9] F. Arrichiello, A. Marino and F. Pierri, “Observer-based decentralized fault detection and isolation strategy for networked multirobot systems,” IEEE Trans. Control Syst. Technol., vol. 23, no. 4, pp. 1465-1476, 2015.
[10] C. Commault, J. M. Dion, O. Sename and R. Moteyian, “Observer-based fault diagnosis for structured systems,” in The 39th IEEE Conference on Decision and Control, Sydney, NSW, Australia, 2000, pp. 4138-4143.
[11] R. Doraiswami and L. Cheded, “Kalman filter for parametric fault detection: an internal model principle-based approach,” IET Control Theory Appl., vol. 6, no. 5, pp. 715-725, 2012.
[12] R. Picatoste, M. Butcher and A. Masi, “Use of Extended Kalman Filter in position sensor fault detection for stepper motors,” in IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, 2016, pp. 1548-1553.
[13] I. Alkov and D. Weidemann, “Fault detection with unscented Kalman filter applied to nonlinear differential-algebraic systems,” in The 18th International Conference on Methods & Models in Automation & Robotics (MMAR), Miedzyzdroje, Poland, 2013, pp. 166-171.
[14] I. Izadi, S. L. Shah and T. Chen, “Parity space fault detection based on irregularly sampled data,” in 2008 American Control Conference, Seattle, WA, USA, 2008, pp. 2798-2803.
[15] H. Wang, Z. Tian, S. Shi and Z. Weng. “A fault detection and isolation scheme based on parity space method for discrete time-delay system,” in The 7th World Congress on Intelligent Control and Automation, Chongqing, China, 2008, pp. 6411-6414.
[16] Y. Li and M. Zhong, “Parity space-based fault detection for linear time-varying systems with multiple packet dropouts,” in 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC), Kaohsiung, China, 2009, pp. 236-239.
[17] E. M. Cimpoesu, B. D. Ciubotaru and D. Stefanoiu, “Fault detection and diagnosis using parameter estimation with recursive least squares,” The 19th International Conference on Control Systems and Computer Science, Bucharest, Romania, 2013, pp. 18-23.
[18] V. Reppa and A. Tzes, “Fault detection and diagnosis based on parameter set estimation,” IET Control Theory Appl., vol. 5, no. 1, pp. 69-83, 2011.
[19] T. Jiang, K. Khorasani and S. Tafazoli, “Parameter estimation-based fault detection, isolation and recovery for nonlinear satellite models,” IEEE Trans. Control Syst. Technol., vol. 16, no. 4, pp. 799-808, 2008.
[20] A. Q. Khan and S. X. Ding, “Threshold computation for robust fault detection in a class of continuous-time nonlinear systems,” 2009 European Control Conference (ECC), Budapest, Hungary, 2009, pp. 3088-3093.
[21] A. Hashemi and P. Pisu, "Adaptive threshold-based fault detection and isolation for automotive electrical systems,” The 9th World Congress on Intelligent Control and Automation, Taipei, Taiwan, 2011, pp. 1013-1018.
[22] J. Saijai, S. X. Ding, A. Abdo, B. Shen and W. Damlakhi, “Threshold computation for fault detection in linear discrete-time Markov jump systems,” Int. J. Adapt. Control Signal Process., vol. 28, no. 11, pp. 1106-1127, 2014.
[23] J. Saijai, A. Abdo, W. Damlakhi and S. X. Ding, “Fault detection scheme for discrete-time Markov jump linear systems with mode-independent residual,” 2012 IEEE International Conference on Control Applications (CCA), Dubrovnik, Croatia, 2012, pp. 1674-1679.
[24] M. Zhong, S. X. Ding, J. Lam and H. Wang, “An LMI approach to design robust fault detection filter for uncertain LTI systems,” Automatica, vol. 39, no. 3, pp. 543-550, 2003.
[25] A. Abdo, S. X. Ding, W. Damlakhi and J. Saijai, “Robust fault detection filter design for uncertain switched systems with adaptive threshold setting,” 2011 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, USA, 2011, pp. 5467-5472.
[26] S. Zhang, Z. Wang, D. Ding, H. Shu, T. Hayat and A. M. Dobaie, “On Design of Robust Fault Detection Filter in Finite-Frequency Domain with Regional Pole Assignment,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 62, no. 4, pp. 382-386, 2015.
[27] J. Davies, H. Tsunashima, R. M. Goodall, R. Dixon, T. Steffen, “Fault detection in high redundancy actuation using an interacting multiple-model approach,” IFAC Proc. Volumes, vol. 42, no. 8, pp. 1228-1233, 2008.
[28] Y. Zhang and X. R. Li, “Detection and diagnosis of sensor and actuator failures using IMM estimator,” IEEE Trans. Aerosp. Electron. Syst., vol. 34, no. 4, pp. 1293-1313, 1998.
[29] S. Zhao, B. Huang and F. Liu, “Fault detection and diagnosis of multiple-model systems with mismodeled transition probabilities,” IEEE Trans. Ind. Electron., vol. 62, no. 8, pp. 5063-5071, 2015.
[30] S. R. Mohanty, A. K. Pradhan and A. Routray, “A cumulative sum-based fault detector for power system relaying application,” IEEE Trans. Power Delivery, vol. 23, no. 1, pp. 79-86, 2008.
[31] Y. Vidya Sagar, A. K. Mishra, A. P. Tiwari and S. B. Degweker, “Online fault detection and diagnosis of in-core neutron detectors using generalized likelihood ratio method,” IEEE Trans. Nucl. Sci., vol. 62, no. 6, pp. 3311-3323, 2015.
[32] E. A. Sikora, “Detection of bearing damage by statistic vibration analysis (diagnosis using the excess, the concept of crest factor),” 2015 International Conference on Mechanical Engineering, Automation and Control Systems (MEACS), Tomsk, Russia, 2015, pp. 1-5.
[33] S. Singh, A. Kumar and N. Kumar, “Motor current signature analysis for bearing fault detection in mechanical systems,” Procedia Mater. Sci., vol. 6, pp. 171-177, 2014.
[34] J. Burriel-Valencia, R. Puche-Panadero, J. Martinez-Roman, A. Sapena-Bano and M. Pineda-Sanchez, “Short-frequency fourier transform for fault diagnosis of induction machines working in transient regime,” IEEE Trans. Instrum. Meas., vol. 66, no. 3, pp. 432-440, 2017.
[35] L. A. Pereira, D. Fernandes, D. S. Gazzana, F. B. Libano and S. Haffner, “Application of the Welch, Burg and MUSIC Methods to the Detection of Rotor Cage Faults of Induction Motors,” 2006 IEEE/PES Transmission & Distribution Conference and Exposition: Latin America, Caracas, Venezuela, 2006, pp. 1-6.
[36] S. Rajakarunakaran, P. Venkumar, D. Devaraj, K. Surya Prakasa Rao, “Artificial neural network approach for fault detection in rotary system,” Appl. Soft Comput., vol. 8, no. 1, pp. 740-748, 2008.
[37] Z. Peng, P. Wei, Z. Lina and W. Dezhi, “Applications of adaptive genetic algorithm to radar engine fault diagnosis,” 2010 Chinese Control and Decision Conference, Xuzhou, China, 2010, pp. 25-29.
[38] R. J. Patton, J. Chen and G. P. Liu, “Robust fault detection of dynamic systems via genetic algorithms,” First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Sheffield, UK, 1995, pp. 511-516.
[39] D. Y. Jung, S. M. Lee, Hong-mei Wang, J. H. Kim and S. H. Lee, “Fault detection method with PCA and LDA and its application to induction motor,” J. Cent. South Univ. T., vol. 17, no. 6, pp. 1238-1242, 2010.
[40] X. Zhang, S. Chen, Y. Zhu and W. Yan, “Fault detection and diagnosis for steam turbine based on kernel GDA,” Proceedings of 2011 International Conference on Modelling, Identification and Control, Shanghai, China, 2011, pp. 58-62.
[41] D. Garcia-Alvarez, M. J. Fuente, P. Vega, G. Sainz, “Fault Detection and Diagnosis using Multivariate Statistical Techniques in a Wastewater Treatment Plant,” IFAC Proc. Volumes, vol. 42, no. 11, pp. 952-957, 2009.
[2] R. Isermann, Fault Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Heidelberg, Berlin, Springe-Verlag 2006.
[3] L. H Chiang, E. L. Russell and R. D. Braatz, Fault Detection and Diagnosis in Industrial Systems. London, Springer-Verlag, 2001.
[4] P. Weber and S. Gentil, “Fault detection using parameter estimation applied to a winding machine,” In IAR Annual Conference, Duisburg, Germany, 1997, pp. 1-6.
[5] O. Moseler and R. Isermann, “Application of model-based fault detection to a brushless DC motor,” IEEE Trans. Ind. Electron., vol. 47, no. 5, pp. 1015-1020, 2000.
[6] Y. Chetouani, “Change detection in a distillation column based on the generalized likelihood ratio approach,” J. Chem. Eng. & Proc. Tech., vol. 2, no. 5, pp. 1-6, 2011.
[7] A. Hwas and R. Katebi, “Nonlinear observer-based fault detection and isolation for wind turbines,” The 22nd Mediterranean Conference on Control and Automation (MED), Palermo, Italy, 2014, pp. 870-375.
[8] P. Zhang, S. X. Ding and P. Liu, “A lifting Based approach to oObserver based fFault detection of linear periodic systems,” IEEE Trans. Autom. Control, vol. 57, no. 2, pp. 457-462, 2012.
[9] F. Arrichiello, A. Marino and F. Pierri, “Observer-based decentralized fault detection and isolation strategy for networked multirobot systems,” IEEE Trans. Control Syst. Technol., vol. 23, no. 4, pp. 1465-1476, 2015.
[10] C. Commault, J. M. Dion, O. Sename and R. Moteyian, “Observer-based fault diagnosis for structured systems,” in The 39th IEEE Conference on Decision and Control, Sydney, NSW, Australia, 2000, pp. 4138-4143.
[11] R. Doraiswami and L. Cheded, “Kalman filter for parametric fault detection: an internal model principle-based approach,” IET Control Theory Appl., vol. 6, no. 5, pp. 715-725, 2012.
[12] R. Picatoste, M. Butcher and A. Masi, “Use of Extended Kalman Filter in position sensor fault detection for stepper motors,” in IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, Italy, 2016, pp. 1548-1553.
[13] I. Alkov and D. Weidemann, “Fault detection with unscented Kalman filter applied to nonlinear differential-algebraic systems,” in The 18th International Conference on Methods & Models in Automation & Robotics (MMAR), Miedzyzdroje, Poland, 2013, pp. 166-171.
[14] I. Izadi, S. L. Shah and T. Chen, “Parity space fault detection based on irregularly sampled data,” in 2008 American Control Conference, Seattle, WA, USA, 2008, pp. 2798-2803.
[15] H. Wang, Z. Tian, S. Shi and Z. Weng. “A fault detection and isolation scheme based on parity space method for discrete time-delay system,” in The 7th World Congress on Intelligent Control and Automation, Chongqing, China, 2008, pp. 6411-6414.
[16] Y. Li and M. Zhong, “Parity space-based fault detection for linear time-varying systems with multiple packet dropouts,” in 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC), Kaohsiung, China, 2009, pp. 236-239.
[17] E. M. Cimpoesu, B. D. Ciubotaru and D. Stefanoiu, “Fault detection and diagnosis using parameter estimation with recursive least squares,” The 19th International Conference on Control Systems and Computer Science, Bucharest, Romania, 2013, pp. 18-23.
[18] V. Reppa and A. Tzes, “Fault detection and diagnosis based on parameter set estimation,” IET Control Theory Appl., vol. 5, no. 1, pp. 69-83, 2011.
[19] T. Jiang, K. Khorasani and S. Tafazoli, “Parameter estimation-based fault detection, isolation and recovery for nonlinear satellite models,” IEEE Trans. Control Syst. Technol., vol. 16, no. 4, pp. 799-808, 2008.
[20] A. Q. Khan and S. X. Ding, “Threshold computation for robust fault detection in a class of continuous-time nonlinear systems,” 2009 European Control Conference (ECC), Budapest, Hungary, 2009, pp. 3088-3093.
[21] A. Hashemi and P. Pisu, "Adaptive threshold-based fault detection and isolation for automotive electrical systems,” The 9th World Congress on Intelligent Control and Automation, Taipei, Taiwan, 2011, pp. 1013-1018.
[22] J. Saijai, S. X. Ding, A. Abdo, B. Shen and W. Damlakhi, “Threshold computation for fault detection in linear discrete-time Markov jump systems,” Int. J. Adapt. Control Signal Process., vol. 28, no. 11, pp. 1106-1127, 2014.
[23] J. Saijai, A. Abdo, W. Damlakhi and S. X. Ding, “Fault detection scheme for discrete-time Markov jump linear systems with mode-independent residual,” 2012 IEEE International Conference on Control Applications (CCA), Dubrovnik, Croatia, 2012, pp. 1674-1679.
[24] M. Zhong, S. X. Ding, J. Lam and H. Wang, “An LMI approach to design robust fault detection filter for uncertain LTI systems,” Automatica, vol. 39, no. 3, pp. 543-550, 2003.
[25] A. Abdo, S. X. Ding, W. Damlakhi and J. Saijai, “Robust fault detection filter design for uncertain switched systems with adaptive threshold setting,” 2011 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, USA, 2011, pp. 5467-5472.
[26] S. Zhang, Z. Wang, D. Ding, H. Shu, T. Hayat and A. M. Dobaie, “On Design of Robust Fault Detection Filter in Finite-Frequency Domain with Regional Pole Assignment,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 62, no. 4, pp. 382-386, 2015.
[27] J. Davies, H. Tsunashima, R. M. Goodall, R. Dixon, T. Steffen, “Fault detection in high redundancy actuation using an interacting multiple-model approach,” IFAC Proc. Volumes, vol. 42, no. 8, pp. 1228-1233, 2008.
[28] Y. Zhang and X. R. Li, “Detection and diagnosis of sensor and actuator failures using IMM estimator,” IEEE Trans. Aerosp. Electron. Syst., vol. 34, no. 4, pp. 1293-1313, 1998.
[29] S. Zhao, B. Huang and F. Liu, “Fault detection and diagnosis of multiple-model systems with mismodeled transition probabilities,” IEEE Trans. Ind. Electron., vol. 62, no. 8, pp. 5063-5071, 2015.
[30] S. R. Mohanty, A. K. Pradhan and A. Routray, “A cumulative sum-based fault detector for power system relaying application,” IEEE Trans. Power Delivery, vol. 23, no. 1, pp. 79-86, 2008.
[31] Y. Vidya Sagar, A. K. Mishra, A. P. Tiwari and S. B. Degweker, “Online fault detection and diagnosis of in-core neutron detectors using generalized likelihood ratio method,” IEEE Trans. Nucl. Sci., vol. 62, no. 6, pp. 3311-3323, 2015.
[32] E. A. Sikora, “Detection of bearing damage by statistic vibration analysis (diagnosis using the excess, the concept of crest factor),” 2015 International Conference on Mechanical Engineering, Automation and Control Systems (MEACS), Tomsk, Russia, 2015, pp. 1-5.
[33] S. Singh, A. Kumar and N. Kumar, “Motor current signature analysis for bearing fault detection in mechanical systems,” Procedia Mater. Sci., vol. 6, pp. 171-177, 2014.
[34] J. Burriel-Valencia, R. Puche-Panadero, J. Martinez-Roman, A. Sapena-Bano and M. Pineda-Sanchez, “Short-frequency fourier transform for fault diagnosis of induction machines working in transient regime,” IEEE Trans. Instrum. Meas., vol. 66, no. 3, pp. 432-440, 2017.
[35] L. A. Pereira, D. Fernandes, D. S. Gazzana, F. B. Libano and S. Haffner, “Application of the Welch, Burg and MUSIC Methods to the Detection of Rotor Cage Faults of Induction Motors,” 2006 IEEE/PES Transmission & Distribution Conference and Exposition: Latin America, Caracas, Venezuela, 2006, pp. 1-6.
[36] S. Rajakarunakaran, P. Venkumar, D. Devaraj, K. Surya Prakasa Rao, “Artificial neural network approach for fault detection in rotary system,” Appl. Soft Comput., vol. 8, no. 1, pp. 740-748, 2008.
[37] Z. Peng, P. Wei, Z. Lina and W. Dezhi, “Applications of adaptive genetic algorithm to radar engine fault diagnosis,” 2010 Chinese Control and Decision Conference, Xuzhou, China, 2010, pp. 25-29.
[38] R. J. Patton, J. Chen and G. P. Liu, “Robust fault detection of dynamic systems via genetic algorithms,” First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Sheffield, UK, 1995, pp. 511-516.
[39] D. Y. Jung, S. M. Lee, Hong-mei Wang, J. H. Kim and S. H. Lee, “Fault detection method with PCA and LDA and its application to induction motor,” J. Cent. South Univ. T., vol. 17, no. 6, pp. 1238-1242, 2010.
[40] X. Zhang, S. Chen, Y. Zhu and W. Yan, “Fault detection and diagnosis for steam turbine based on kernel GDA,” Proceedings of 2011 International Conference on Modelling, Identification and Control, Shanghai, China, 2011, pp. 58-62.
[41] D. Garcia-Alvarez, M. J. Fuente, P. Vega, G. Sainz, “Fault Detection and Diagnosis using Multivariate Statistical Techniques in a Wastewater Treatment Plant,” IFAC Proc. Volumes, vol. 42, no. 11, pp. 952-957, 2009.