Electronic Returnless Fuel System Fault Diagnosis and Isolation: A Data-Driven Approach

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Bharath Pattipati Krishna Pattipati Youssef A. Ghoneim Mark Howell Mutasim Salman

Abstract

The Electronic Return-less Fuel System (ERFS) manages the delivery of fuel from the fuel tank to the engine. The pressure in the fuel line is electronically controlled by the fuel system control module by speeding up or slowing down the fuel pump. This allows the system to efficiently control the amount of fuel provided to the engine when compared to vehicles equipped with a standard fuel system wherein the fuel pump continuously runs at full speed. A failure in the fuel system that impacts the ability to deliver fuel to the engine will have an immediate effect on system performance. Consequently, improved reliability and availability, and reduction in the number of walk-home situations require efficient fault detection, isolation and prognosis of the ERFS system. This paper develops and implements data-driven fault detection, isolation and severity estimation algorithms for the ERFS. The HIL Fuel System Rig and a Chevrolet Silverado truck were used to collect and analyze the fuel system behavior under different fault conditions. Several data-driven classifiers, such as support vector machines, K- nearest Neighbor, Discriminant analysis, Bayes classifier, Partial- least squares, Quadratic and Linear classifiers, were implemented on a limited set of data for both training and testing. Regression techniques, such as Partial least squares regression and Principle component regression, are used to estimate the severity of faults. The resulting solution approach has the potential to be applicable to a wide variety of systems, ranging from automobiles to aerospace systems.

How to Cite

Pattipati, B. ., Pattipati, K. ., A. Ghoneim, Y., Howell, M. ., & Salman, M. . (2013). Electronic Returnless Fuel System Fault Diagnosis and Isolation: A Data-Driven Approach. Annual Conference of the PHM Society, 5(1). https://doi.org/10.36001/phmconf.2013.v5i1.2222
Abstract 28 | PDF Downloads 18

##plugins.themes.bootstrap3.article.details##

Keywords

PHM

References
Bishop, C.M. (2006). Pattern Recognition and Machine Learning. New York, USA: Springer.

Bro, R. (1996). Multiway Calibration. Multilinear PLS. Journal of Chemometrics, vol.10, issue 1, pp. 47-61. doi: 10.1002/(SICI)1099-128X(199601)10:1<47::AID- CEM400>3.0.CO;2-C

Chiang, L. H., Russel, E., & Braatz, R. (2001). Fault Detection and Diagnosis in Industrial Systems. London, U.K.: Springer-Verlag.

Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern Classification. 2nd edition, New York, USA: John Wiley and Sons.

Ge, M., Du, R., Zhang, G., & Xu, Y. (2004). Fault diagnosis using support vector machine with an application in sheet metal stamping operations. Mechanical Systems and Signal Processing, vol. 18, pp.143-159. doi: 10.1016/S0888-3270(03)00071-2

Jackson, J. E. (1991). A User’s Guide to Principal Components. New York, USA: John Wiley & Sons.

Luo, J., Tu, H., Pattipati, K., Qiao, L., & Chigusa, S. (2005). Graphical models for diagnosis knowledge representation and inference. IEEE Autotestcon, pp. 483- 489. doi: 10.1109/MIM.2006.1664042

Luo, J., Tu, H., Pattipati, K., Qiao, L., & Chigusa, S. (2006). Diagnosis knowledge representation and inference. IEEE Instrumentation and Measurement Magazine, vol. 9, issue 4, pp. 45–52. doi: 10.1109/MIM.2006.1664042

Namburu, S. M., Azam, M. S., Luo, J., Choi, K., & Pattipati, K. R. (2007). Data-driven modeling, fault diagnosis, and optimal sensor selection for HVAC chillers. IEEE
transactions on automation science and engineering, vol. 4, no. 3. doi: 10.1109/TASE.2006.888053

Smola, A. J., Bartlett, P. L., Scholkopf, B., & Schuurmans, D. (2000). Advances in Large Margin Classifiers. Cambridge, Massachusetts: The MIT Press.

Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York, USA: Springer.
Section
Technical Papers