Model Based Detection And Isolation Of Faults Of Diesel Engines
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Model-Based Detection and Isolation of Faults of Diesel Engines
Author: Alexander Schilling
language: de
Publisher: Sudwestdeutscher Verlag Fur Hochschulschriften AG
Release Date: 2009
The increasingly stringent limitations on emission levels imply more narrow tolerances of operations, such that diesel engines have to be continuously monitored in order to ensure the optimality of the operating conditions. For this purpose, the knowledge of the engine outputs is a fundamental prerequisite. This knowledge could be gained either with real sensors or with virtual ones, i.e., with real-time mathematical models. Currently, the only engine-output sensors commercially available are those for measuring Lambda and the NOx concentration level. The aim of this work is thus to explore the possibilities given by the aforementioned engine-output sensors for the detection and isolation of faults in the air and fuel paths of diesel engines. To achieve this objective a model-based strategy is pursued. First, a mathematical model of the engine is developed. Successively, control-oriented models for the real-time computation of the Lambda value and the NOx concentration are derived from the detailed combustion model. Finally, on the basis of the control-oriented models developed, the fault detection and isolation system is realized.
Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019)
This book includes original, peer-reviewed research papers from the 11th International Conference on Modelling, Identification and Control (ICMIC2019), held in Tianjin, China on July 13-15, 2019. The topics covered include but are not limited to: System Identification, Linear/Nonlinear Control Systems, Data-driven Modelling and Control, Process Modelling and Process Control, Fault Diagnosis and Reliable Control, Intelligent Systems, and Machine Learning and Artificial Intelligence.The papers showcased here share the latest findings on methodologies, algorithms and applications in modelling, identification, and control, integrated with Artificial Intelligence (AI), making the book a valuable asset for researchers, engineers, and university students alike.
Fault Diagnosis of Hybrid Dynamic and Complex Systems
Online fault diagnosis is crucial to ensure safe operation of complex dynamic systems in spite of faults affecting the system behaviors. Consequences of the occurrence of faults can be severe and result in human casualties, environmentally harmful emissions, high repair costs, and economical losses caused by unexpected stops in production lines. The majority of real systems are hybrid dynamic systems (HDS). In HDS, the dynamical behaviors evolve continuously with time according to the discrete mode (configuration) in which the system is. Consequently, fault diagnosis approaches must take into account both discrete and continuous dynamics as well as the interactions between them in order to perform correct fault diagnosis. This book presents recent and advanced approaches and techniques that address the complex problem of fault diagnosis of hybrid dynamic and complex systems using different model-based and data-driven approaches in different application domains (inductor motors, chemical process formed by tanks, reactors and valves, ignition engine, sewer networks, mobile robots, planetary rover prototype etc.). These approaches cover the different aspects of performing single/multiple online/offline parametric/discrete abrupt/tear and wear fault diagnosis in incremental/non-incremental manner, using different modeling tools (hybrid automata, hybrid Petri nets, hybrid bond graphs, extended Kalman filter etc.) for different classes of hybrid dynamic and complex systems.