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A Fault Diagnosis of Engine Starting System Via Starter Motors Using Fuzzy Logic Algorithm

Year 2011, Volume: 24 Issue: 3, 437 - 449, 25.11.2011

Raif Bayir , Omer Bay

Abstract

Uncertainties in the system models, the presence of noise and the stochastic behavior of several variables reduce the reliability and robustness of the fault diagnosis methods. For overcoming these kinds of problems, this study proposes the fault diagnosis of starter motors based on fuzzy logic methodology. A starter motor is a serial wound dc motor which is used for running the Internal Combustion Engine (ICE). If a fault occurs with the starter motor, the ICE cannot be run. Especially in emergency vehicles (such as ambulance, fire engine, etc), starter motor faults causes any other faults. In this study, a fuzzy logic based fault detection system has been developed for implementation on emergency vehicles. Information of the current and the voltage of a starter motor is acquired and then practiced on a fuzzy logic fault diagnosis system (FLFDS). For this purpose, a graphical user interface (GUI) software is developed by using Visual Basic 6.0 programming language. FLFDS is effective in detection of six types of starter motor faults. The proposed system can be used in a Quality Control unit of manufacturers and maintenance-repairing units.

 

Key words: Engine starting system; Starter motor faults; Fault diagnosis, Fuzzy logic.

 

Keywords

Engine starting system; Starter motor faults; Fault diagnosis Fuzzy logic.

References

  • Peter, V., “Parameter Estimation, Condition Monitoring and Diagnosis of Electrical Machines”, New York, Clarendon Press, Oxford, (1993).
  • Subhasis, N. and Hamid A., T.,. “Condition Monitoring and Fault Diagnosis of Electrical Machines - a Review”, in Proc. Industry App. Conf. th IAS Annual Meeting, 1, 197-204, (1999).
  • Steffen, L, and Mihiar, A., “Methods of Fault Diagnosis, Control Eng. Practice, 5, 5, 683-692, (1997).
  • Isermann, R. “Supervision, Fault-Detection and Fault–Diagnosis Methods an Introduction”, Control Eng. Practice, 5, 5, 639-652, (1997).
  • Xiao Z. G. and Seppo J. O., “Soft Computing Methods in Motor Fault Diagnosis”, Applied Soft Comp., 1, 73-81, (2001).
  • Nandi, S.; Toliyat, H.A.; Xiaodong Li;”Condition monitoring and fault diagnosis of electrical motors-a review”, Conversion, 20, 4, 719–729, (2005) on Energy
  • Miguel, L. J, Blázquez, L. F., “Fuzzy logic-based motor” , Engineering Applications of Artificial Intelligence, 18 , 4 , 423-450 (2005)
  • Engineering: A Tutorial”, Proceeding of the IEEE, , 3, 345-377, (1995).
  • Daniel G. S., George J. K., Harold W. L. and Yoshinori E., “Application of Fuzzy Sets and Approximate Reasoning”, Proc. the IEEE. 82, 4, 495, (1994).
  • Isermann, R. “On Fuzzy Logic Applications for Automatic Control, Supervision and Fault Diagnosis”, IEEE Trans. Sys., Man and Cyber., Part A, 28, 2, 221-235, (1998).
  • Dominik F. and Rolf I., “Hierarchical Motor Diagnosis Utilizing Structural Knowledge and a Self-Learning Neuro-Fuzzy Scheme”, IEEE Trans. Ind. Elec. 47, 5, 1070-1077, (2000).
  • Olaf, M. and Rolf, I., “Application of Model- Based Fault Detection to a Brushless DC Motor”, IEEE Trans.Ind. Elect, 47, 5, 1015-1020 (2000).
  • Igor, G., “Fault Diagnosis and Prevention by Fuzzy Sets”, IEEE Transaction on Reliability, R-34, 4, 382-388, (1985).
  • Hamid N. and Mohamed El Hachemi B., Application of fuzzy logic to induction motors condition monitoring, IEEE Power Eng., Rev. , 52–54, (1999).
  • Xiang-Qun L., Hong-Yue, Z., Jun, L. and Jing, Y., “Fault Detection and Diagnosis of Permanent- Magnet DC Motor Based on Parameter Estimation Transactions on Industrial Electronics, 47, 5, 1030, (2000). Network”, IEEE
  • Bay, Ö.F., Bayir, R., “Fault Diagnosis of Starter Motors Using Fuzzy Logic”, 3rd. International Advanced Technologies Symposium, Ankara, Turkiye, (2003) Klaus, B., Automotive Electric/Electronic GmbH, Systems, Germany, 346-375, (1995). Stuttgart
  • Tom, D., Automobile Electrical and Electronic System, 2. Edition, Arnold Pub., Printed in Great Britain, (2000).
  • Herbert, E. E, Automotive Electrical Systems, nd Edition, Prentice-Hall Inc., New Jersey, USA, (1985).
  • William, H. C., Automotive Electronics and Electrical Equipment. 10th Edition, McGraw-Hill Inc., USA, (1986).
  • Young, A. P., Griffiths, L. and Fardon, G. E., Automobile Electrical and Electronic Equipment., England, Butler & Tanner Ltd., (1980).

Year 2011, Volume: 24 Issue: 3, 437 - 449, 25.11.2011

Raif Bayir , Omer Bay

Abstract

References

  • Peter, V., “Parameter Estimation, Condition Monitoring and Diagnosis of Electrical Machines”, New York, Clarendon Press, Oxford, (1993).
  • Subhasis, N. and Hamid A., T.,. “Condition Monitoring and Fault Diagnosis of Electrical Machines - a Review”, in Proc. Industry App. Conf. th IAS Annual Meeting, 1, 197-204, (1999).
  • Steffen, L, and Mihiar, A., “Methods of Fault Diagnosis, Control Eng. Practice, 5, 5, 683-692, (1997).
  • Isermann, R. “Supervision, Fault-Detection and Fault–Diagnosis Methods an Introduction”, Control Eng. Practice, 5, 5, 639-652, (1997).
  • Xiao Z. G. and Seppo J. O., “Soft Computing Methods in Motor Fault Diagnosis”, Applied Soft Comp., 1, 73-81, (2001).
  • Nandi, S.; Toliyat, H.A.; Xiaodong Li;”Condition monitoring and fault diagnosis of electrical motors-a review”, Conversion, 20, 4, 719–729, (2005) on Energy
  • Miguel, L. J, Blázquez, L. F., “Fuzzy logic-based motor” , Engineering Applications of Artificial Intelligence, 18 , 4 , 423-450 (2005)
  • Engineering: A Tutorial”, Proceeding of the IEEE, , 3, 345-377, (1995).
  • Daniel G. S., George J. K., Harold W. L. and Yoshinori E., “Application of Fuzzy Sets and Approximate Reasoning”, Proc. the IEEE. 82, 4, 495, (1994).
  • Isermann, R. “On Fuzzy Logic Applications for Automatic Control, Supervision and Fault Diagnosis”, IEEE Trans. Sys., Man and Cyber., Part A, 28, 2, 221-235, (1998).
  • Dominik F. and Rolf I., “Hierarchical Motor Diagnosis Utilizing Structural Knowledge and a Self-Learning Neuro-Fuzzy Scheme”, IEEE Trans. Ind. Elec. 47, 5, 1070-1077, (2000).
  • Olaf, M. and Rolf, I., “Application of Model- Based Fault Detection to a Brushless DC Motor”, IEEE Trans.Ind. Elect, 47, 5, 1015-1020 (2000).
  • Igor, G., “Fault Diagnosis and Prevention by Fuzzy Sets”, IEEE Transaction on Reliability, R-34, 4, 382-388, (1985).
  • Hamid N. and Mohamed El Hachemi B., Application of fuzzy logic to induction motors condition monitoring, IEEE Power Eng., Rev. , 52–54, (1999).
  • Xiang-Qun L., Hong-Yue, Z., Jun, L. and Jing, Y., “Fault Detection and Diagnosis of Permanent- Magnet DC Motor Based on Parameter Estimation Transactions on Industrial Electronics, 47, 5, 1030, (2000). Network”, IEEE
  • Bay, Ö.F., Bayir, R., “Fault Diagnosis of Starter Motors Using Fuzzy Logic”, 3rd. International Advanced Technologies Symposium, Ankara, Turkiye, (2003) Klaus, B., Automotive Electric/Electronic GmbH, Systems, Germany, 346-375, (1995). Stuttgart
  • Tom, D., Automobile Electrical and Electronic System, 2. Edition, Arnold Pub., Printed in Great Britain, (2000).
  • Herbert, E. E, Automotive Electrical Systems, nd Edition, Prentice-Hall Inc., New Jersey, USA, (1985).
  • William, H. C., Automotive Electronics and Electrical Equipment. 10th Edition, McGraw-Hill Inc., USA, (1986).
  • Young, A. P., Griffiths, L. and Fardon, G. E., Automobile Electrical and Electronic Equipment., England, Butler & Tanner Ltd., (1980).
There are 20 citations in total.

Details

Primary Language English
Journal Section Electrical & Electronics Engineering
Authors

Raif Bayir

Omer Bay

Publication Date November 25, 2011
Published in Issue Year 2011 Volume: 24 Issue: 3

Cite

APA Bayir, R., & Bay, O. (2011). A Fault Diagnosis of Engine Starting System Via Starter Motors Using Fuzzy Logic Algorithm. Gazi University Journal of Science, 24(3), 437-449.
AMA Bayir R, Bay O. A Fault Diagnosis of Engine Starting System Via Starter Motors Using Fuzzy Logic Algorithm. Gazi University Journal of Science. November 2011;24(3):437-449.
Chicago Bayir, Raif, and Omer Bay. “A Fault Diagnosis of Engine Starting System Via Starter Motors Using Fuzzy Logic Algorithm”. Gazi University Journal of Science 24, no. 3 (November 2011): 437-49.
EndNote Bayir R, Bay O (November 1, 2011) A Fault Diagnosis of Engine Starting System Via Starter Motors Using Fuzzy Logic Algorithm. Gazi University Journal of Science 24 3 437–449.
IEEE R. Bayir and O. Bay, “A Fault Diagnosis of Engine Starting System Via Starter Motors Using Fuzzy Logic Algorithm”, Gazi University Journal of Science, vol. 24, no. 3, pp. 437–449, 2011.
ISNAD Bayir, Raif - Bay, Omer. “A Fault Diagnosis of Engine Starting System Via Starter Motors Using Fuzzy Logic Algorithm”. Gazi University Journal of Science 24/3 (November 2011), 437-449.
JAMA Bayir R, Bay O. A Fault Diagnosis of Engine Starting System Via Starter Motors Using Fuzzy Logic Algorithm. Gazi University Journal of Science. 2011;24:437–449.
MLA Bayir, Raif and Omer Bay. “A Fault Diagnosis of Engine Starting System Via Starter Motors Using Fuzzy Logic Algorithm”. Gazi University Journal of Science, vol. 24, no. 3, 2011, pp. 437-49.
Vancouver Bayir R, Bay O. A Fault Diagnosis of Engine Starting System Via Starter Motors Using Fuzzy Logic Algorithm. Gazi University Journal of Science. 2011;24(3):437-49.