Model based Online Fault Diagnosis of Automotive Engines using Joint State and Parameter Estimation



Published Oct 18, 2015
Nadeer E P Amit Patra Siddhartha Mukhopadhyay


In this work, an Extended Kalman Filter (EKF) based tunable diagnoser, which uses a minimal hybrid nonlinear state space model of a spark ignition (SI) four stroke engine, is used for the detection and isolation of a variety of engine system faults including intake manifold leak, injector fault and exhaust manifold leak. The state estimates and innovation sequences from the EKF based estimator are shown to be adequate for the detection and isolation of the faults under consideration. Once a fault is detected and isolated, the diagnoser could be tuned online to perform fault identification by redefining a model/fault parameter as an additional state to be estimated and then performing a joint state and parameter estimation. The engine model and diagnoser are implemented in SimulinkTM and are validated against an AMESimTM model of the engine. For the nominal engine model, the performance of the EKF estimator is compared with two other computationally more expensive nonlinear estimators, namely the Unscented Kalman Filter (UKF) and Rao-Blackwell Particle Filter (RBPF).

How to Cite

E P, N., Patra, A. ., & Mukhopadhyay, S. . (2015). Model based Online Fault Diagnosis of Automotive Engines using Joint State and Parameter Estimation. Annual Conference of the PHM Society, 7(1).
Abstract 166 | PDF Downloads 131




Annand, W. J. (1963). Heat transfer in the cylinder of reciprocating internal combustion engines. Proceedings of the IMechE, Part D: Journal of Automobile Engineering, (pp. 973–990).

Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. Signal Processing, IEEE Transactions on, 50(2), 174-188.

Bar-Shalom, Y., Li, X. R., & Kirubarajan, T. (2004).Estimation with applications to tracking and navigation: theory algorithms and software. John Wiley & Sons.

Basseville, M., & Nikiforov, I. V. (1993). Detection of abrupt changes: theory and application (Vol. 104). Prentice Hall Englewood Cliffs.

De Freitas, N. (2002). Rao-Blackwellised particle filtering for fault diagnosis. Aerospace Conference Proceedings, 2002. IEEE, 4, pp. 4-1767.

Doucet, A., De Freitas, N., Gordon, N., & others. (2001). Sequential Monte Carlo methods in practice (Vol. 1). Springer New York.

Guzzella, L., & Onder, C. H. (2010). Introduction to modeling and control of internal combustion engine systems. Springer.

Haykin, S. S. (Ed.). (2001). Kalman filtering and neural networks. Wiley Online Library.

Heywood, J. B. (1998). Internal Combustion Engine Fundamentals. McGraw-Hill International Editions.

Hutter, F., Dearden, R., & others. (2003). The gaussian particle filter for diagnosis of non-linear systems. Proceedings of the 5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes.

Sarkka, S. (2006). Recursive Bayesian inference on stochastic differential equations. Helsinki University of Technology.

Sengupta, S., Mukhopadhyay, S., & Deb, A. K. (2011). Instantaneous within cycle model based fault estimators for SI engines. India Conference (INDICON), 2011 Annual IEEE, (pp. 1-6).

Sengupta, S., Mukhopadhyay, S., Deb, A., Pattada, K., & De, S. (2012). Hybrid Automata Modeling of SI Gasoline Engines towards State Estimation for Fault Diagnosis. SAE International Journal of Engines, 5(3), 759-781.
Technical Research Papers