Preview

Mekhatronika, Avtomatizatsiya, Upravlenie

Advanced search

Comparative Analysis of Canonical Forms in Fault Diagnosis and Estimation Problems

https://doi.org/10.17587/mau.23.289-294

Abstract

The  paper considers the methods  of different  canonical  forms application  to the problems of fault  diagnosis and  estimation  in technical  systems described by linear dynamic  models under  disturbances.  Identification and  Jordan  canonical forms are investigated.  The  main  relations describing fault diagnosis and estimation  problems for different canonical  forms are given, and  comparative  analysis  of possibility of their application  is performed.  An analysis  shows that the identification canonical  form produces relations enable developing algorithms for the diagnostic observer and estimator design while Jordan canonical  form assumes using some heuristic methods.  It was shown that Jordan canonical  form is more preferable to guarantee  full disturbance  decoupling,  that is invariance  with respect to the disturbance.  On the other hand,  when full decoupling is impossible, the identification  canonical  form enables developing algorithm of partial decoupling while Jordan canonical  form assumes using some heuristic methods. The advantage  of Jordan canonical  form is that it ensures stability of the designed system based on properties of the matrix  describing this form while the identification  canonical  form assumes using feedback  based on the residual which must be generated. This allows for Jordan canonical  form to reduce the dimension of the designed diagnostic observer and estimator.  The  new method  to guarantee sensitivity of the diagnostic observer to the faults is developed.  The  method  is based on analysis  of the observability matrix  and new rules to calculate  matrices describing the diagnostic observer. Theoretical  results are illustrated by practical example  of well known  three tank  system.

About the Authors

A. N. Zhirabok
Far Eastern Federal University; Institute of Marine Technology Problems
Russian Federation

Aleksei N. Zhirabok - Dr.  of Sci., Professor, Far  Eastern Federal University.

Vladivostok, 690950.



C. I. Kim
Far Eastern Federal University
Russian Federation

Vladivostok, 690950.



E. Yu. Bobko
Far Eastern Federal University
Russian Federation

Vladivostok, 690950.



References

1. Mironivskii L. A. Functional diagnosis in dynamic systems, Moscow, Publishing house of MSU, 1998, 256 p. (in Russian).

2. Shumsky А., Zhirabok A. Methods for fault diagnosis and fault tolerant control in dynamic systems, Vladivostok, Publishing house of FESTU, 2018, 173 p. (in Russian).

3. Blanke M., Kinnaert M., Lunze J., Staroswiecki M. Diagnosis and Fault-Tolerant Control, Berlin, Springer-Verlag, 2006.

4. Witczak M. Fault diagnosis and fault tolerant control strategies for nonlinear systems, Berlin, Springer, 2014.

5. Elsobet T., Bregon A., Pulodo B., Puig V. Fault diagnosis of dynamic systems, Berlin, Springer, 2019.

6. Efimov D., Polyakov A., Richard J. Interval observer design for estimation and control of time-delay descriptor systems, European Journal of Control, 2015, vol. 23, pp. 26—35.

7. Kolesov N., Gruzlikov A., Lukoyanov E. Using fuzzy interacting observers for fault diagnosis in systems with parametric uncertainty, Proceedings of XII-th Inter. Symp. Intelligent Systems, INTELS’16, 5-7 October 2016, Moscow, Russia, pp. 499—504.

8. Zhirabok A., Zuev A., Shumsky A. Diagnosis of linear dynamic systems: an approach based on sliding mode observers, Automation and Remote Control, vol. 81, 2020, pp. 18—35.

9. Zhirabok A., Zuev A., Bobko E., Filatov A. Fault accommodation problem solution in nonlinear systems using linear methods, Mekhatronika, Avtomatizatsiya, Upravlenie, 2020, vol. 21, no. 1, pp. 21—27.

10. Zhirabok A., Zuev A., Filaretov V. Fault identification in underwater vehicle thrusters via sliding mode observers, Int. Journal of Applied Mathematics and Computer Science, 2020, vol. 30, no. 4, pp. 679—688.

11. Low X., Willsky A., Verghese G. Optimally robust redundancy relations for failure detection in uncertain systems, Automatica, vol. 22, 1996, pp. 333—344.

12. Heredia G., Ollero A. Virtual sensor for failure detection, identification and recovery in the transition phase of a morphing aircraft, Sensors, 2010, vol. 10, pp. 2188—2201.

13. Luzar M., Witczak M. Fault-tolerant control and diagnosis for LPV system with H-infinity virtual sensor, Proceedings of 3rd Conf. Control and Fault-Tolerant Systems, 2016. Barcelona, Spain, pp. 825—830.

14. Jove E., Casteleiro-Roca J., Quntian H., Mendez-Perez J., Calvo-Rolle J. Virtual sensor for fault detection, isolation and data recovery for bicomponent mixing machine monitoring, Informatica, 2019, vol. 30, no. 4, pp. 671—687.

15. Hosseinpoor Z., Arefi M., Razavi-Far R., Mozafari N., Hazbavi S. Virtual sensors for fault diagnosis: a case of induction motor broken rotor bar, IEEE Sensors Journal, 2021, vol. 21, no. 4, pp. 5044—5051.

16. Zhirabok A., Kim C. Virtual sensors in the fault diagnosis problem, Mekhatronika, Avtomatizatsiya, Upravlenie, 2021, vol. 22, no. 6, pp. 298—303.


Review

For citations:


Zhirabok A.N., Kim C.I., Bobko E.Yu. Comparative Analysis of Canonical Forms in Fault Diagnosis and Estimation Problems. Mekhatronika, Avtomatizatsiya, Upravlenie. 2022;23(6):289-294. (In Russ.) https://doi.org/10.17587/mau.23.289-294

Views: 313


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1684-6427 (Print)
ISSN 2619-1253 (Online)