Abstract
A new on-line fault detection and isolation (FDI) scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in this paper. The neural classifier is adaptive to cope with the significant parameter uncertainty, disturbances, and environment changes. The developed scheme is capable of diagnosing faults in on-line mode and can be directly implemented in an on-board diagnosis system (hardware). The robustness of the FDI for the closed-loop system with crankshaft speed feedback is investigated by testing it for a wide range of operational modes including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all these changes occurring at the same time. The evaluations are performed using a mean value engine model (MVEM), which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Official Journal of the European Communities L 350/1. Directive 98/69/EC of the European Parliament and of the Council of 13 October 1998, Relating to Measures to be Taken against Air Pollution by Emissions from Motor Vehicles and Amending Council Directive 70/220/EEC, 1998.
C. Evans-Pughe. Learning to Drive [Tightening Emissions Regulations]. Engineering & Technology, vol. 1, no. 2, pp. 42–45, 2006.
Y. Tan, M. Saif. Neural-networks-based Nonlinear Dynamic Modelling for Automotive Engines. Neurocomputing, vol. 30, no. 1, pp. 129–142, 2000.
F. Kimmich, A. Schwarte, R. Isermann. Fault Detection for Modern Diesel Engines Using Signal and Process Modelbased Methods. Control Engineering Practice, vol. 13, no. 2, pp. 189–203, 2005.
C. Manzie, M. Palaniswami, H. Watson. Gaussian Networks for Fuel Injection Control. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 215, no. 10, pp. 1053–1068, 2001.
S. Jakubek, T. Strasser. Fault Diagnosis Using Neural Networks with Ellipsoidal Basis Functions. In Proceedings of American Control Conference, IEEE Press, Anchorage, USA, vol. 5, pp. 3846–3851, 2002.
J. J. Gertler. M. Costine, X. W. Fang, R. Hira, Z. Kowalczuk, Q. Luo. Model-based On-board Fault Detection and Diagnosis for Automotive Engines, Control Engineering Practice, vol. 1, no. 1, pp. 3–17, 1993.
D. Antory. Application of a Data-driven Monitoring Technique to Diagnose Air Leaks in an Automotive Diesel Engine: A Case Study. Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 795–808, 2007.
S. J. Lee, K. Park, T. H. Hwang, J. H. Hwang, Y. C. Jung, Y. J. Kim. Development of Hardware-in-the-loop Simulation System as a Testbench for ESP Unit. International Journal of Automotive Technology, vol. 8, no. 2, pp. 203–209, 2007.
O. F. Bay, R. Bayir. Kohonen Network Based Fault Diagnosis and Condition Monitoring of Pre-engaged Starter Motors. International Journal of Automotive Technology, vol. 6, no. 4, pp. 341–350, 2005.
D. Antory, U. Kruger, G. Irwin, G. McCullough. Fault Diagnosis in Internal Combustion Engines Using Non-linear Multivariate Statistics. Proceedings of the Institution of Mechanical Engineers — Part I: Journal of Systems and Control Engineering, vol. 219, no. 14, pp. 243–258, 2005.
P. J. Shayler, M. Goodman, T. Ma. The Exploitation of Neural Networks in Automotive Engine Management Systems. Engineering Applications of Artificial Intelligence, vol. 13, no. 2, pp. 147–157, 2000.
J. D. Wu, J. C. Chen. Continuous Wavelet Transform Technique for Fault Signal Diagnosis of Internal Combustion Engines. NDT & E International, vol. 39, no. 4, pp. 304–311, 2006.
D. L. Yu, J. B. Gomm. Implementation of Neural Network Predictive Control to Multivariable Chemical Reactor. Control Engineering Practice, vol. 11, no. 11, pp. 1315–1323, 2003.
X. D. Zhang, M. M. Polycarpou, T. Parisini. A Robust Detection and Isolation Scheme for Abrupt and Incipient Faults in Nonlinear Systems. IEEE Transactions on Automatic Control, vol. 47, no. 4, pp. 576–590, 2002.
M. Nyberg, T. Stutte. Model Based Diagnosis of the Air Path of an Automotive Diesel Engine. Control Engineering Practice, vol. 12, no. 5, pp. 513–525, 2004.
D. Capriglione, C. Liguori, C. Pianese, A. Pietrosanto. On Line Sensor Fault Detection, Isolation and Accommodation in Automotive Engines. IEEE Transactions on Instrumentation and Measurement, vol. 52, no. 4, pp. 1182–1189, 2003.
M. S. Sangha, D. L. Yu, J. B. Gomm. On-board Monitoring and Diagnosis for Spark Ignition Engine Air Path via Adaptive Neural Networks. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 220, no. 11, pp. 1641–1655, 2006.
J. Moody, C. Darken. Fast Learning in Networks of Locallytuned Processing Units, Neural Computation, vol. 1, no. 2, pp. 281–294, 1989.
L. Ljung. System Identification: Theory for the User, 2nd ed., Prentice-Hall, Englewood Cliffs, NJ, USA, pp. 361–369, 1999.
E. Hendricks, D. Engler, M. Fam. A Generic Mean Value Engine Model for Spark Ignition Engines, Technical Report, Institute of Automation, Denmark Technical University, [Online], Avaiable: http://www.iau.dtu.dk/:_eh/, 2000.
M. Reineman. Effectiveness of OBD II Evaporative Emission Monitors-30 Vehicle Study, U.S. Environmental Protection Agency Report EPA420-R-00-018, 2000.
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by Universities UK, Faculty of Technology and Environment and School of Engineering, Liverpool John Moores University, UK.
Mahavir Singh Sangha received first class B. Eng. (Electrical) degree form University of Jodhpur, India, in 1991 and M. Sc. (Intelligent Control) degree with distinction from Liverpool John Moores University (LJMU), UK, in 2005. He is presently a Ph.D. candidate at LJMU, Liverpool, UK. He worked in an electricity distribution company for about 12 years.
His research interests include fault diagnosis in an automotive engine and neural networks.
Dingli Yu received the B.Eng. degree from Harbin University of Civil Engineering, China, in 1982, the master degree from Jilin University of Technology (JUT), China, in 1986, and the Ph.D. degree from Coventry University, UK, in 1995, all in electrical engineering. He was a lecturer at JUT from 1986 to 1990 before he came to University of Salford as a visiting researcher in 1991. He then worked at Liverpool John Moores University as a post-doctoral researcher since 1995 and became a lecturer in 1998, where he is currently a professor of control systems.
His research interests include fault detection and fault tolerant control of bilinear and nonlinear systems, adaptive neural networks and their control applications, and model predictive control for chemical processes and engine systems.
J. Barry Gomm received the B.Eng. first class degree in electrical and electronic engineering in 1987 and the Ph.D. degree in process fault detection in 1991 from Liverpool John Moores University (LJMU), UK. He joined the academic staff at LJMU in 1991 and is a reader in intelligent control systems.
His research interests include neural networks for modelling, control and fault diagnosis of non-linear processes, intelligent methods for control, system identification, adaptive systems, chemical process, and automotive applications.
Rights and permissions
About this article
Cite this article
Sangha, M.S., Yu, D. & Gomm, J.B. Robustness assessment and adaptive FDI for car engine. Int. J. Autom. Comput. 5, 109–118 (2008). https://doi.org/10.1007/s11633-008-0109-9
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11633-008-0109-9