Fault detection for modern Diesel engines using signal- and process model-based methods

Abstract Modern Diesel engines with direct fuel injection and turbo charging have shown a significant progress in fuel consumption, emissions and driveability. Together with exhaust gas recirculation and variable geometry turbochargers they became complicated and complex processes. Therefore, fault detection and diagnosis is not easily done and need to be improved. This contribution shows a systematic development of fault detection and diagnosis methods for two system components of Diesel engines, the intake system and the injection system together with the combustion process. By applying semiphysical dynamic process models, identification with special neural networks, signal models and parity equations residuals are generated. Detectable deflections of these residuals lead to symptoms which are the basis for the detection of several faults. Experiments with a 2.0 l Diesel engine on a dynamic test bench as well as in the vehicle have demonstrated the detection and diagnosis of several implemented faults in real time with reasonable calculation effort.

[1]  Oliver Nelles,et al.  Nonlinear system identification with local linear neuro-fuzzy models , 1999 .

[2]  Rolf Isermann,et al.  Combustion Supervision by Evaluating the Crankshaft Speed and Acceleration , 2000 .

[3]  Rolf Isermann,et al.  Model Based Injection Mass Supervision for Diesel Engines Using a Fast Broadband Oxygen Sensor , 2002 .

[4]  Rolf Isermann,et al.  MODEL BASED FAULT DETECTION FOR THE INJECTION, COMBUSTION AND ENGINE-TRANSMISSION , 2002 .

[5]  John B. Heywood,et al.  Internal combustion engine fundamentals , 1988 .

[6]  O. Nelles.  Nonlinear System Identification , 2001 .

[7]  M. Klell,et al.  Thermodynamik der Verbrennungskraftmaschine , 2002 .

[8]  Gerald Fischer.  Expertenmodell zur Berechnung der Reibungsverluste von Ottomotoren , 2000 .

[9]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[10]  Rolf Isermann,et al.  Model-based fault detection and diagnosis for diesel engines , 2002 .

[11]  Rolf Isermann,et al.  Model based engine torque supervision for diesel engines using the engine speed signal , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[12]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[13]  Dominik Füssel.  Fault diagnosis with tree structured neuro-fuzzy systems , 2002 .

[14]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[15]  Rolf Isermann,et al.  Modellgestützte Steuerung, Regelung und Diagnose von Verbrennungsmotoren , 2003 .

[16]  Rolf Isermann,et al.  Supervision, fault-detection and fault-diagnosis methods — An introduction , 1997 .

[17]  Rolf Isermann,et al.  Semi-physical modelling of nonlinear processes by means of local model approaches , 2002 .