Frank Kimmich

Model-based fault detection of a diesel engine with turbo charger - a case study

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 2.01 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.

Model-based fault detection and diagnosis for Diesel engines

Due to the increasing complexity of diesel engines with more and more electrical and electronic components and sophisticated control strategies, automatic fault detection and diagnosis is becoming increasingly important. Within the scope of the FVV project “Model-Based Fault Detection and Diagnosis for Diesel Engines” at the Institute of Automatic Control, Darmstadt University of Technology, new model-based methods for monitoring the intake system, the injection, the combustion and the exhaust system have been developed.

MODEL BASED FAULT DETECTION FOR THE INJECTION, COMBUSTION AND ENGINE-TRANSMISSION

Abstract New technologies, rising customer demands and severe exhaust gas regulations led to a rapid advancement of combustion engines. Nowadays, engines are characterized by more and more complex structures, whereby maintenance and trouble shooting becomes more and more complicated. Therefore the development of suitable fault detection and diagnosis methods is necessary, whereas the use of model based methods enhance a definite detection of faults according to type, size and location.

Model based engine torque supervision for diesel engines using the engine speed signal

New technologies, rising customer demands and severe exhaust gas regulations led to a rapid advancement of combustion engines. Nowadays, engines are characterized by more and more complex structures, whereby maintenance and trouble shooting becomes more and more complicated. Therefore the development of suitable fault detection and diagnosis methods is necessary, whereas model based methods enhance a definite detection of faults according to type, size and location.

Modellbasierte Fehlererkennung und -diagnose für Dieselmotoren

Aufgrund der wachsenden Komplexitat des Dieselmotors durch Zunahme von elektrischen/elektronischen Komponenten sowie aufwandiger Steuerung und Regelung erhalt die automatische Fehlererkennung und -diagnose des Dieselmotors eine grosere Bedeutung. Im Rahmen des FVV-Projekts „Modellgestutzte praventive Diagnosemethoden (Fehlerfruherkennung) fur Dieselmotoren“ am Institut fur Automatisierungstechnik der TU Darmstadt wurden daher neue modellbasierte Methoden zur Uberwachung von Ansaugsystem, Einspritzung und Verbrennung sowie Abgassystem entwickelt.

Signal Model Based Fault Diagnosis for Combustion Engines

Abstract The detection of misfires and combustion variations is still a very demanding task. In this contribution different methods for fault detection and diagnosis in sparkignition and Diesel engines are presented. Signal models are used for the generation of relevant symptoms by evaluating the engine crankshaft speed and the exhaust gas pressure. The resulting symptoms are classified both by simple threshold detection and by fuzzy and neuro-fuzzy diagnosis approaches. All results were obtained with real measured data at an engine stand and a test car. It is shown, that a good performance over the entire operating range of the engine can be achieved.

Modellgestützte Fehlerdiagnose der Verbrennung eines Dieselmotors

Die automatische Erkennung und Diagnose von Fehlern bei Diesel- motoren gewinnt mit immer scharferen Abgasnormen und gesetzlichen Forderungen zur On-Board-Diagnose (OBD) ab 2003 sowie mit zunehmender Komplexitat elektronischer Komponenten und aufwendiger Steuerung und Regelung immer mehr an Bedeutung. Modellbasierte Verfahren erlauben hierbei durch Verwendung von analytischem Prozesswissen in Form mathematischer Modelle, interne Prozessgrosen, wie z.B. Parameter oder Zustandsgrosen, zu ermitteln und so auf rechnerischem Weg eine Erkennung und Lokalisierung von Fehlern zu ermoglichen. Im folgenden werden Methoden zur modellbasierten Fehlererkennung fur die Verbrennung und Einspritzung vorgestellt.