This paper presents a new method for on-board fault diagnosis for the air path of spark ignition (SI) engines. The method uses a radial basis function (RBF) neural network to classify predefined possible faults from engine measurements, reporting fault occurrence as well as the type and size of a fault. After diagnosing faults in each sample interval, the weights and widths of the RBF fault classifier are updated with the measurements and appropriately selected target outputs. Consequently, the network can adapt to the time-varying dynamics of the engine and environment change so that the false alarm rate is greatly reduced and the required network size is also reduced. The developed scheme is assessed with various faults simulated on a mean value engine benchmark model and compared with a fixed-parameter RBF classifier. Simulation results demonstrate the effectiveness of the proposed method.
NybergM.StutteT.Model based diagnosis of the air path of an automotive diesel engine. Control Engng Practice, 2004, 12, 513–525.
3.
FrankP. M.Analytical and quantitative model-based fault diagnosis - a survey and some new results. Eur. J. Control, 1996, 2, 6–28.
4.
IsermannR.BalleP.Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engng Practice, 1997, 5 (5), 709–719.
5.
NybergM.NielsenL.Model based diagnosis for the air intake system of SI engine. SAE Trans. J. Comml Veh., 1997, 106, 9–20.
6.
KimmichESchwarteA.IsermannR.Fault detection for modern diesel engines using signal and process model-based methods. Control Engng Practice, 2005, 13, 189–203.
7.
GertlerJ.CostinM.FangX.HiraR.KowalczukZ.LuoQ.Model based on board fault detection and diagnosis for automotive engines. Control Engng Practice, 1993, 1 (1), 3–17.
8.
GertlerJ.CostinM.FangX.KowalczukZ.KunwerM.MonajemyR.Model based diagnosis for automotive engines - algorithm development and testing on a production vehicle. IEEE Trans. Control Syst. Technol., 1995, 3 (1), 61–69.
9.
LuoJ.TuF.AzamM.PattipatiK.WillettP.QiaoL.KawamotoM.Intelligent model-based diagnostics for vehicle health management. Proc. SPIE, 2003, 5107 (Security and Condition Monitoring Issue III), 13–26.
10.
KimY.-W.Automotive engine diagnosis and control via nonlinear estimation. IEEE Control Syst. Mag., 1998, 34, 84–99.
11.
NybergM.KrysanderM.Combining AI, FDI, and statistical hypothesis-testing in a framework for diagnosis. In Proceedings of IFAC Symposium Safeprocess'03, Washington, DC, 2003.
12.
LuYChenQ. T.BrennanH.A fuzzy system for automotive fault diagnosis: Fast rule generation and self-tuning. IEEE Trans. Veh. Technol., 2000, 49 (2), 651–660.
13.
HisselD.PeraC. M.KauffmannM. J.Diagnosis of automotive fuel cell power generator. J. Power Sources, 2004, 128, 239–246.
14.
BarigozziA.MagniL.ScattoliniR.A probabilistic approach to diagnosis of industrial systems. IEEE Trans. Control Syst. Technol., 2004, 12 (6), 950–955.
15.
PrincipeJ. C.EulianoN. R.LefebvreW. C.Neural and adaptive systems: Fundamentals through simulations, 2000 (John Wiley & Sons, Inc., New York).
16.
ManzieC.PalaniswamiM.WatsonH.Gaussian networks for fuel injection control. Proc. Instn Mech. Engrs, Part D: J. Automobile Engineering, 2001, 215, 1053–1068.
17.
JakubekS.StrasserT.Fault diagnosis using neural networks with ellipsoidal basis functions. In Proceedings of American Control Conference, Anchorage, Alaska, 8–10 May 2002, pp. 3846–3851.
18.
CapriglioneD.LiguoriC.PianeseC.PietrosantoA.On line sensor fault detection, isolation and accommodation in automotive engines. IEEE Trans. Instrum. and Measmt, 2003, 52 (4), 1182–1189.
19.
ShaylerJ. P.GoodmanM.MaT.The exploitation of neural networks in automotive engine management systems. Engng Applic. Artif. Intell., 2000, 13, 147–157.
20.
SorsaT.KoivoH. N.Applications of artificial neural networks in process fault diagnosis. Auto-matica, 1993, 29 (4), 843–849.
21.
SanghaM. S.GommJ. B.YuD. L.PageG. F.An investigation of neural networks in fault diagnosis of an automotive engine air path. 2nd ASIM Workshop onModelling, Control and Diagnostics of Combustion Engine Processes, Wismar, Germany, 16–17 September 2004, pp. 23–32.
22.
Hendricks, E., Engler, D., and Fam, M. A generic mean value engine model for spark ignition engines, Institute of Automation, Denmark Technical University, www.iau.dtu.dk/εh/
23.
ReinemanM.Effectiveness of OBD II evaporative emission monitors — 30 Vehicle Study, US Environmental Protection Agency Report EPA420-R-00-018, October 2000.
24.
LjungL.System identification — theory for the user, 2nd edition, 1999, pp. 361–369 (Prentice-Hall, Upper Saddle River, New Jersey).
25.
ZhangX.PolycarpouM. M.ParisiniT.A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems. IEEE Trans. Autom. Control, 2002, 47 (4), 576–593.
