Abstract
This chapter presents an online fault diagnosis scheme for a spark ignition automotive engine, based on a complex physics-based Hybrid System Model (HSM). The overall diagnostic signal processing scheme starts with a continuous state estimation stage comprising a single Extended Kalman Filter (EKF) estimator that employs a nominal normal (i.e. without fault) HSM of the engine in which the nonlinear continuous dynamical model is switched according to the discrete state transition of the HSM. This is followed by a residual prediction stage, and a fault detection and isolation stage. The nonlinear physics-based HSM is such that, its overall continuous state vector comprises elements which have physical interpretation (e.g. temperature, mass fraction, etc.), is an augmentation of state sub-vectors for each component or reservoir of the engine system. Since the variables have physical origin, the transitions between discrete modes of the HSM do not cause any discrete jump in the continuous state vector. Further, the augmentation of component state sub-vectors, elements of which are derived from related thermodynamics, makes it easy to derive the analytical expressions related to the Jacobian matrices needed for EKF. The covariance matrices of the process and measurement noise are also approximately computed online. This ensures that the nominal estimator switches to the correct discrete mode quickly in response to the changes in the plant. Engine faults, such as a leak in the manifolds, injector block, cylinder valve wear, and sensor failures can be captured easily by suitable parameterization of the model. Use of such physics-based parametric models, and reuse of the Jacobian matrices from the nominal estimator, greatly reduce the real-time computational requirements. Using this technique, in the residual prediction stage, the residuals from the nominal estimator under each fault scenario could be predicted for unit magnitude of the faults. In the last stage, a Generalized Likelihood Ratio Test (GLRT) is applied on the predicted residual for each fault and the actual residual from the nominal estimator, to detect and isolate the faults. Propositional logic is used to isolate the fault when multiple fault detection functions are triggered while using the GLRT method. The method is demonstrated on realistic simulations.
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Nyberg, M. (2002). Model-based diagnosis of an automotive engine using several types of fault models. IEEE Transactions on Control Systems Technology, 10(5), 679–689.
Nyberg, M., & Stutte, T. (2004). Model based diagnosis of the air path of an automotive diesel engine. Control Engineering Practice, 12(5), 513–525.
Kim, Y. W., Rizzoni, G., & Utkin, V. (1998). Automotive engine diagnosis and control via nonlinear estimation. IEEE Control Systems, 18(5), 84–99.
Andersson, P., & Eriksson, L. (2002). Detection of exhaust manifold leaks on a turbocharged SI-engine with wastegate (no. 2002-01-0844). SAE Technical Paper.
Bar-Shalom, Y., Li, X. R., & Kirubarajan, T. (2004). Estimation with applications to tracking and navigation: Theory algorithms and software. New York: Wiley.
Doucet, A., de Freitas, N., & Gordon, N. (2001). An introduction to sequential Monte Carlo methods. In A. Doucet, N. de Freitas, & N. Gordon (Eds.), Sequential Monte Carlo methods in practice, Statistics for engineering and information science. New York, NY: Springer.
Doucet, A., De Freitas, N., Murphy, K., & Russell, S. (2000, June). Rao-Blackwellised particle filtering for dynamic Bayesian networks. In Proceedings of the sixteenth conference on uncertainty in artificial intelligence (pp. 176–183). San Francisco: Morgan Kaufmann Publishers Inc.
Andersson, P., & Eriksson, L. (2001). Air-to-cylinder observer on a turbocharged SI-engine with wastegate (no. 2001-01-0262). SAE Technical Paper.
Nyberg, M., & Nielsen, L. (1997). Model based diagnosis for the air intake system of the SI-engine (no. 970209). SAE Technical Paper.
Shiao, Y., & Moskwa, J. J. (1995). Cylinder pressure and combustion heat release estimation for SI engine diagnostics using nonlinear sliding observers. IEEE Transactions on Control Systems Technology, 3(1), 70–78.
Yan, F., & Wang, J. (2012). Design and robustness analysis of discrete observers for diesel engine in-cylinder oxygen mass fraction cycle-by-cycle estimation. IEEE Transactions on Control Systems Technology, 20(1), 72–83.
Chen, P., & Wang, J. (2013). Observer-based estimation of air-fractions for a diesel engine coupled with aftertreatment systems. IEEE Transactions on Control Systems Technology, 21(6), 2239–2250.
Buckland, J. H., Freudenberg, J., Grizzle, J. W., & Jankovic, M. (2009, June). Practical observers for unmeasured states in turbocharged gasoline engines. In American control conference. ACC’09 (pp. 2714–2719). IEEE.
Xue, W., Bai, W., Yang, S., Song, K., Huang, Y., & Xie, H. (2015). ADRC with adaptive extended state observer and its application to air–fuel ratio control in gasoline engines. IEEE Transactions on Industrial Electronics, 62(9), 5847–5857.
Butt, Q. R., & Bhatti, A. I. (2008). Estimation of gasoline-engine parameters using higher order sliding mode. IEEE Transactions on Industrial Electronics, 55(11), 3891–3898.
Iqbal, M., Bhatti, A. I., Ayubi, S. I., & Khan, Q. (2011). Robust parameter estimation of nonlinear systems using sliding-mode differentiator observer. IEEE Transactions on Industrial Electronics, 58(2), 680–689.
Sengupta, S., Mukhopadhyay, S., Deb, A., Pattada, K., & De, S. (2011). Hybrid automata modeling of SI gasoline engines towards state estimation for fault diagnosis. SAE International Journal of Engines, 5(3), 759–781.
Nadeer, E. P., Patra, A., & Mukhopadhyay, S. (2015). Model based online fault diagnosis of automotive engines using joint state and parameter estimation. In 2015 annual conference of the prognostics and health management society, Coronado, CA, USA.
Schilling, A., Amstutz, A., & Guzzella, L. (2008). Model-based detection and isolation of faults due to ageing in the air and fuel paths of common-rail direct injection diesel engines equipped with a λ and a nitrogen oxides sensor. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 222(1), 101–117.
Riggins, R. N., & Rizzoni, G. (1990, May). The distinction between a special class of multiplicative events and additive events: Theory and application to automotive failure diagnosis. In American control conference (pp. 2906–2911). IEEE.
Vasu, J. Z., Deb, A. K., & Mukhopadhyay, S. (2015). MVEM-based fault diagnosis of automotive engines using Dempster–Shafer theory and multiple hypotheses testing. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(7), 977–989.
Pernestål, A. (2009). Probabilistic fault diagnosis with automotive applications. Doctoral dissertation, Linköping University Electronic Press.
Pattipati, K., Kodali, A., Luo, J., Choi, K., Singh, S., Sankavaram, C., & Qiao, L. (2008). An integrated diagnostic process for automotive systems. In Computational intelligence in automotive applications (pp. 191–218). Berlin: Springer.
Sangha, M. S., Yu, D. L., & Gomm, J. B. (2006). 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, 220(11), 1641–1655.
Felder, R. M., & Rousseau, R. W. (2008). Elementary principles of chemical processes. New York: Wiley.
Guzzella, L., & Onder, C. H. (2010). Introduction to modeling and control of internal combustion engine systems. Berlin: Springer.
Heywood, J. B. (1988). Internal combustion engine fundamentals (Vol. 930). New York: Mcgraw-Hill.
Annand, W. J. D. (1963). Heat transfer in the cylinders of reciprocating internal combustion engines. Proceedings of the Institution of Mechanical Engineers, 177(1), 973–996.
Simon, D. (2006). Optimal state estimation: Kalman, H infinity, and nonlinear approaches. Hoboken, NJ: Wiley.
Särkkä, S. (2006). Recursive Bayesian inference on stochastic differential equations. Espoo, Finland: Helsinki University of Technology.
Mohamed, A. H., & Schwarz, K. P. (1999). Adaptive Kalman filtering for INS/GPS. Journal of Geodesy, 73(4), 193–203.
Haykin, S. S. (Ed.). (2001). Kalman filtering and neural networks (p. 304). New York: Wiley.
Kay, S. M. (1998). Fundamentals of statistical signal processing: Detection theory (Vol. 2). Upper Saddle River, NJ: Prentice Hall.
Sayed-Mouchaweh, M., & Lughofer, E. (2015). Decentralized fault diagnosis approach without a global model for fault diagnosis of discrete event systems. International Journal of Control, 88(11), 2228–2241.
Van Trees, H. L. (2001). Detection, estimation, and modulation theory, part I: Detection, estimation, and linear modulation theory. New York: Wiley.
Sayed-Mouchaweh, M. (2016). Learning from data streams in dynamic environments, Springer briefs in electrical and computer engineering. Cham: Springer.
Toubakh, H., & Sayed-Mouchaweh, M. (2016). Hybrid dynamic classifier for drift-like fault diagnosis in a class of hybrid dynamic systems: Application to wind turbine converters. Neurocomputing, 171, 1496–1516.
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Nadeer, E.P., Mukhopadhyay, S., Patra, A. (2018). Hybrid System Model Based Fault Diagnosis of Automotive Engines. In: Sayed-Mouchaweh, M. (eds) Fault Diagnosis of Hybrid Dynamic and Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-74014-0_7
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