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Fault diagnosis of a nonlinear hybrid system using adaptive unscented Kalman filter bank

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Abstract

In this paper, a model-based fault diagnosis scheme of a nonlinear hybrid system using an adaptive unscented Kalman filter (AUKF) bank is proposed. The hybrid system is an amalgamation of discrete dynamics and continuous states. Fault diagnosis for simultaneous occurrences of multiple faults such as leakage fault, clogging fault, sensor fault, and actuator fault on a benchmark three-tank system are simulated. The residual signal based output generates some discrete modes that guarantee the uniqueness of the concerning fault. The efficacy of the proposed scheme is compared with that of the adaptive extended Kalman filter (AEKF) bank on the same system to prove its better response over AEKF.

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References

  1. Yen GG, Ho L (2003) Online multiple-model-based fault diagnosis and accommodation. IEEE Trans Industr Electron 50(2):296–312

    Article  Google Scholar 

  2. Eide P, Maybeck P (1995) Implementation and demonstration of a multiple model adaptive estimation failure detection system for the F-16. Proceedings of 34th IEEE Conference on Decision Control, New Orleans, LA, USA, pp 1873–1878

  3. Eide P, Maybeck P (1996) An MMAE failure detection system for the F-16. IEEE Trans Aerosp Electron Syst 32(3):1125–1136

    Article  Google Scholar 

  4. Singh A, Izadian A, Anwar S (2013) Fault diagnosis of li-ion batteries using multiple-model adaptive estimation. In: Proceedings 39th IEEE IECON, Vienna, Austria, pp. 3524–3529

  5. Singh A, Izadian A, Anwar S (2014) Nonlinear model based fault detection of lithium ion battery using multiple model adaptive estimation. 19th World Congress. International Federation of Automatic Control 47(3):8546–8551

  6. Rupp D, Ducard G, Shafa E et al (2005) Extended multiple model adaptive estimation for the detection of sensor and actuator faults. In: Proceeding of 44th IEEE CDC-ECC, pp 3079–3084

  7. Hajiyev C, Soken HE (2013) Robust adaptive Kalman filter for estimation of UAV dynamics in the presence of sensor /actuator faults. Aerosp Sci Technol 28(1):376–383

    Article  Google Scholar 

  8. Izadian A (2013) Self-tuning fault diagnosis of MEMS. Mechatronics 23(8):1094–1099

    Article  Google Scholar 

  9. Loebis D, Sutton R, Chudley J et al (2004) Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system. Control Eng Pract 12(12):1531–1539

    Article  Google Scholar 

  10. Gertler J (1988) Survey of model-based failure detection and isolation in complex plants. IEEE Control Syst Mag 8(6):3–11

    Article  Google Scholar 

  11. Chen J, Patton RJ (1999) Robust model-based fault diagnosis for dynamic systems. In: The International Series on Asian Studies in Computer and Information Science, Kluwer Academic Press, Dordrecht, ch 9, pp 251–295

  12. Ding SX (2008) Model-based fault diagnosis techniques: design schemes, algorithms tools. Advances in Industrial Control Springer-Verlag, Berlin, Germany, ch 15, pp 471–489

  13. Wang S, Tian X, Fang H (2019) Event-based state and fault estimation for nonlinear systems with logarithmic quantization and missing measurements. J Franklin Inst 356(7):4076–4096

    Article  MathSciNet  Google Scholar 

  14. Chen J, Zhang H (2007) Robust fault detection of faulty actuators via Unknown input observers. Int J Syst Sci 22(10):1829–1839

    Article  Google Scholar 

  15. Zarei J, Shokri E (2014) Robust sensor fault detection based on nonlinear unknown input observer. Measurement 48(2):355–367

    Article  Google Scholar 

  16. Mondal S, Chakraborty G, Bhattacharyya K (2008) Robust unknown input observer for nonlinear systems and its application to fault detection and isolation. J Dyn Sys-T ASME 130(4):044503–044505

    Article  Google Scholar 

  17. Wang D, Lum K (2007) Adaptive unknown input observer approach for aircraft actuator fault detection and isolation. Intl J Adapt Control Signal Process 21(1):31–48

    Article  MathSciNet  Google Scholar 

  18. Caccavale F, Pierri F, Villani L (2008) Adaptive observer for fault diagnosis in nonlinear discrete-time systems. J Dyn Sys-T ASME 130(2):021005–021009

    Article  Google Scholar 

  19. Zhang X, Polycarpo M, Parisini T (2010) Fault diagnosis of a class of nonlinear systems with Lipschitz nonlinearities using adaptive estimation. Automatica 46(2):290–299

    Article  MathSciNet  Google Scholar 

  20. Zhang X, Tang L, Decastro J (2013) Robust fault diagnosis of aircraft engines: a nonlinear adaptive estimation-based approach. IEEE Trans Control Syst Technol 21(3):861–868

    Article  Google Scholar 

  21. Methnani S, Lafont F, Gauthier J et al (2013) Actuator and sensor fault detection, isolation and identification in nonlinear dynamical systems, with an application to a waste water treatment plant. J Comput Eng Inf Technol 1(4):112–125

    Google Scholar 

  22. Fan J, Zhang Y, Zheng Z (2013) Adaptive observer-based integrated fault diagnosis and fault-tolerant control systems against actuator faults and saturation. J Dyn Sys-T ASME 135(4):041008–041013

    Article  Google Scholar 

  23. Gaeid K, Ping H (2010) Induction motor fault detection and isolation through unknown input observer. Sci Res Essays 5(20):3152–3159

    Google Scholar 

  24. Chen W, Chen WT, Saif M et al (2014) Simultaneous fault isolation and estimation of lithium-ion batteries via synthesized design of Luenberger and learning observers. IEEE Trans Control Syst Technol 22(1):290–298

    Article  Google Scholar 

  25. Efimov D, Raïssi T, Zolghadri A (2013) Set adaptive observers for linear parameter-varying systems: application to fault detection. J Dyn Sys-T ASME 136 (2):021006–021007

    Article  Google Scholar 

  26. Zhanga Y, Wang Z, Ma L et al (2019) Annulus-event-based fault detection, isolation and estimation for multirate time-varying systems: applications to a three-tank system. J Process Control 75:48–58

    Article  Google Scholar 

  27. Mendonca LF, Sousa JMC, Costa JMG Sa da (2008) Fault accommodation of an Experimental three tank system using fuzzy predictive control. In: Proceeding of the IEEE International Conference on Fuzzy Systems, Hong Kong, pp 1619–1625

  28. Heiming B, Lunze J (1999) Definition of the three-tank benchmark problem for controller reconfiguration. In: Proceeding of the European Control Conference, Karlsruhe, Germany

  29. Zhou DH, Wang GZ, Ding SX (2000) Sensor fault tolerant control of nonlinear systems with application to three-tank-systems. In: 4th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, Budapest, Hungary, pp 810–815

  30. Chatterjee S, Sadhu S, Ghoshal TK (2015) Fault detection and identification of non-linear hybrid system using self-switched sigma point filter bank. IET Control Theory Appl 9(7):1093–1102

    Article  Google Scholar 

  31. Theilliol D, Noura H, Ponsart JC (2002) Fault diagnosis and accommodation of a three tank system based on analytical redundancy. ISA Trans 41(3):365–382

    Article  Google Scholar 

  32. Mirzaee A, Salahshoor K (2012) Fault diagnosis and accommodation of nonlinear systems based on multiple-model adaptive unscented Kalman filter and switched MPC and H-infinity loop-shaping controller. J Process Control 22(3):626–634

    Article  Google Scholar 

  33. Villez K, Srinivasan B, Rengaswamy R et al (2011) Kalman-based strategies for Fault detection and identification (FDI): extensions and critical evaluation for a buffer tank system. Comput Chem Eng 35(5):806–816

    Article  Google Scholar 

  34. Zhou DH, Xiao He, Wang Z et al (2012) Leakage fault diagnosis for an internet-based three-tank system: an experimental study. IEEE Trans Control Syst Technol 20(4):857–870

    Article  Google Scholar 

  35. Mrugalski M, Luzar M, Pazera M et al (2016) Neural network based robust actuator fault diagnosis for a non-linear multitank system. ISA Trans 61:318–328

    Article  Google Scholar 

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Authors and Affiliations

  1. Electrical Engineering Department, MCKV Institute of Engineering, Liluah, Howrah, West Bengal, 711204, India

    Chandrani Sadhukhan

  2. Electronic and Telecommunication Engineering Department, MCKV Institute of Engineering, Liluah, Howrah, West Bengal, 711204, India

    Swarup Kumar Mitra

  3. Electronic and Telecommunication Engineering Department, Jadavpur University, Jadavpur, West Bengal, 700032, India

    Mrinal Kanti Naskar

  4. Department of Mechanical and Aeronautical Engineering, University of Pretoria, Pretoria, 0002, South Africa

    Mohsen Sharifpur

  5. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Mohsen Sharifpur

Authors
  1. Chandrani Sadhukhan
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  2. Swarup Kumar Mitra
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  3. Mrinal Kanti Naskar
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  4. Mohsen Sharifpur
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Correspondence to Chandrani Sadhukhan or Mohsen Sharifpur.

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Sadhukhan, C., Mitra, S.K., Naskar, M.K. et al. Fault diagnosis of a nonlinear hybrid system using adaptive unscented Kalman filter bank. Engineering with Computers 38, 2717–2728 (2022). https://doi.org/10.1007/s00366-020-01235-0

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  • DOI: https://doi.org/10.1007/s00366-020-01235-0

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