Residual generator fuzzy identification for automotive diesel engine fault diagnosis

Silvio Simani

International Journal of Applied Mathematics and Computer Science (2013)

  • Volume: 23, Issue: 2, page 419-438
  • ISSN: 1641-876X

Abstract

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Safety in dynamic processes is a concern of rising importance, especially if people would be endangered by serious system failure. Moreover, as the control devices which are now exploited to improve the overall performance of processes include both sophisticated control strategies and complex hardware (input-output sensors, actuators, components and processing units), there is an increased probability of faults. As a direct consequence of this, automatic supervision systems should be taken into account to diagnose malfunctions as early as possible. One of the most promising methods for solving this problem relies on the analytical redundancy approach, in which residual signals are generated. If a fault occurs, these residual signals are used to diagnose the malfunction. This paper is focused on fuzzy identification oriented to the design of a bank of fuzzy estimators for fault detection and isolation. The problem is treated in its different aspects covering the model structure, the parameter identification method, the residual generation technique, and the fault diagnosis strategy. The case study of a real diesel engine is considered in order to demonstrate the effectiveness the proposed methodology.

How to cite

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Silvio Simani. "Residual generator fuzzy identification for automotive diesel engine fault diagnosis." International Journal of Applied Mathematics and Computer Science 23.2 (2013): 419-438. <http://eudml.org/doc/257076>.

@article{SilvioSimani2013,
abstract = {Safety in dynamic processes is a concern of rising importance, especially if people would be endangered by serious system failure. Moreover, as the control devices which are now exploited to improve the overall performance of processes include both sophisticated control strategies and complex hardware (input-output sensors, actuators, components and processing units), there is an increased probability of faults. As a direct consequence of this, automatic supervision systems should be taken into account to diagnose malfunctions as early as possible. One of the most promising methods for solving this problem relies on the analytical redundancy approach, in which residual signals are generated. If a fault occurs, these residual signals are used to diagnose the malfunction. This paper is focused on fuzzy identification oriented to the design of a bank of fuzzy estimators for fault detection and isolation. The problem is treated in its different aspects covering the model structure, the parameter identification method, the residual generation technique, and the fault diagnosis strategy. The case study of a real diesel engine is considered in order to demonstrate the effectiveness the proposed methodology.},
author = {Silvio Simani},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {fault detection and isolation; analytical redundancy; Takagi-Sugeno fuzzy prototypes; residual generator fuzzy modelling and identification; real diesel engine},
language = {eng},
number = {2},
pages = {419-438},
title = {Residual generator fuzzy identification for automotive diesel engine fault diagnosis},
url = {http://eudml.org/doc/257076},
volume = {23},
year = {2013},
}

TY - JOUR
AU - Silvio Simani
TI - Residual generator fuzzy identification for automotive diesel engine fault diagnosis
JO - International Journal of Applied Mathematics and Computer Science
PY - 2013
VL - 23
IS - 2
SP - 419
EP - 438
AB - Safety in dynamic processes is a concern of rising importance, especially if people would be endangered by serious system failure. Moreover, as the control devices which are now exploited to improve the overall performance of processes include both sophisticated control strategies and complex hardware (input-output sensors, actuators, components and processing units), there is an increased probability of faults. As a direct consequence of this, automatic supervision systems should be taken into account to diagnose malfunctions as early as possible. One of the most promising methods for solving this problem relies on the analytical redundancy approach, in which residual signals are generated. If a fault occurs, these residual signals are used to diagnose the malfunction. This paper is focused on fuzzy identification oriented to the design of a bank of fuzzy estimators for fault detection and isolation. The problem is treated in its different aspects covering the model structure, the parameter identification method, the residual generation technique, and the fault diagnosis strategy. The case study of a real diesel engine is considered in order to demonstrate the effectiveness the proposed methodology.
LA - eng
KW - fault detection and isolation; analytical redundancy; Takagi-Sugeno fuzzy prototypes; residual generator fuzzy modelling and identification; real diesel engine
UR - http://eudml.org/doc/257076
ER -

References

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  1. Babuška, R. (1998). Fuzzy Modeling for Control, Kluwer Academic Publishers, Boston, MA. 
  2. Babuška, R. (2000). Fuzzy Modelling and Identification Toolbox, Version 3.1, Control Engineering Laboratory, Faculty of Information Technology and Systems, Delft University of Technology, Delft, http://lcewww.et.tudelft.nl/˜babuska. 
  3. Bonfè, M., Castaldi, P., Mimmo, N. and Simani, S. (2011). Active fault tolerant control of nonlinear systems: The cart-pole example, International Journal of Applied Mathematics and Computer Science 21(3): 441-455, DOI: 10.2478/v10006-011-0033-y. Zbl1234.93054
  4. Bosch, R. (2006). Diesel-Engine Management, 4 Edn., Wiley, Weinheim. 
  5. Chen, J. and Patton, R.J. (1999). Robust Model-Based Fault Diagnosis for Dynamic Systems, Kluwer Academic Publishers, Boston, MA. Zbl0920.93001
  6. Ding, S.X. (2008). Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools, 1st Edn., Springer, Berlin/Heidelberg. 
  7. Fantuzzi, C. and Rovatti, R. (1996). On the approximation capabilities of the homogeneous Takagi-Sugeno model, Proceedings of the 5th IEEE International Conference on Fuzzy Systems, New Orleans, LA, USA, pp. 1067-1072. 
  8. Fantuzzi, C., Simani, S., Beghelli, S. and Rovatti, R. (2002). Identification of piecewise affine models in noisy environment, International Journal of Control 75(18): 1472-1485. Zbl1039.93011
  9. Isermann, R. (2005). Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, 1st Edn., Springer-Verlag, Weinheim. 
  10. Jain, T., Yamè, J.J. and Sauter, D. (2012). Model-free reconfiguration mechanism for fault tolerance, International Journal of Applied Mathematics and Computer Science 22(1): 125-137, DOI: 10.2478/v10006-012-0009-6. Zbl1273.93051
  11. Korbicz, J., Koscielny, J. M., Kowalczuk, Z. and Cholewa, W. (Eds.) (2004). Fault Diagnosis: Models, Artificial Intelligence, Applications, Springer-Verlag, London. Zbl1074.93004
  12. Ljung, L. (1997). System Identification Toolbox for Use with MATLAB R User's Guide, The MathWorks, Inc., Natick, MA. 
  13. Ljung, L. (1999). System Identification: Theory for the User, 2nd Edn., Prentice Hall, Englewood Cliffs, NJ. Zbl0615.93004
  14. Marusak, P.M. and Tatjewski, P. (2008). Actuator fault tolerance in control systems with predictive constrained set-point optimizers, International Journal of Applied Mathematics and Computer Science 18(4): 539-551, DOI: 10.2478/v10006-008-0047-2. Zbl1155.93357
  15. Pulkrabek, W.W. (2003). Engineering Fundamentals of the Internal Combustion Engine, 2nd Edn., Prentice Hall, Richmond, TX. 
  16. Rovatti, R. (1996). Takagi-Sugeno models as approximators in Sobolev norms: The SISO case, 5th IEEE International Conference on Fuzzy Systems, New Orleans LA, USA, Vol. 2, pp. 1060-1066. 
  17. Rovatti, R., Fantuzzi, C. and Simani, S. (2000). High-speed DSP-based implementation of piecewise-affine and piecewise-quadratic fuzzy systems, Signal Processing Journal 80(6): 951-963. Zbl1034.94530
  18. Simani, S. (2007). Fault diagnosis of a simulated industrial gas turbine via identification approach, International Journal of Adaptive Control and Signal Processing 21(4): 326-353, DOI: 10.1002/acs.924. Zbl1113.93081
  19. Simani, S., Fantuzzi, C. and Beghelli, S. (2000). Diagnosis techniques for sensor faults of industrial processes, IEEE Transactions on Control Systems Technology 8(5): 848-855. 
  20. Simani, S., Fantuzzi, C. and Patton, R.J. (2003). Modelbased Fault Diagnosis in Dynamic Systems Using Identification Techniques, Advances in Industrial Control, Vol. 1, Springer-Verlag, London. 
  21. Simani, S., Fantuzzi, C., Rovatti, R. and Beghelli, S. (1999). Parameter identification for piecewise linear fuzzy models in noisy environment, International Journal of Approximate Reasoning 1(22): 149-167. Zbl0994.93519
  22. Stamatis, D.H. (2003). Failure Mode and Effect Analysis: FMEA from Theory to Execution, 2nd Edn., ASQ Quality Press, Milwaukee, WI. 
  23. Svard, C. and Nyberg, M. (2010a). Residual generators for fault diagnosis using computation sequences with mixed causality applied to automotive systems, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 40(6): 1310-1328, DOI: 10.1109/TSMCA.2010.2049993. 
  24. Svard, C. and Nyberg, M. (2010b). Residual generators for fault diagnosis using computation sequences with mixed causality applied to automotive systems, IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 40(6): 1310-1328, DOI: 10.1109/TSMCA.2010.2049993. 
  25. Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its application to modeling and control, IEEE Transaction on System, Man and Cybernetics SMC15(1): 116-132. Zbl0576.93021
  26. Van Huffel, S. and Lemmerling, P. (Eds.) (2002). Total Least Squares and Errors-in-Variables Modeling: Analysis, Algorithms and Applications, 1st Edn., Springer-Verlag, London. Zbl0984.00011
  27. Xu, D., Jiang, B. and Shi, P. (2012). Nonlinear actuator fault estimation observer: An inverse system approach via a T-S fuzzy model, International Journal of Applied Mathematics and Computer Science 22(1): 183-196, DOI: 10.2478/v10006-012-0014-9. Zbl1273.93105

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