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Title:Model-based fault detection and diagnosis of selective catalytic reduction systems for diesel engines
Author(s):Chen, Rui
Director of Research:Wang, Xinlei
Doctoral Committee Chair(s):Wang, Xinlei
Doctoral Committee Member(s):Hansen, Alan C.; Lee, Chia-Fon; Domínguez-García, Alejandro D.
Department / Program:Engineering Administration
Discipline:Agricultural & Biological Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Selective Catalytic Reduction (SCR)
Nitrogen Oxides (NOx)
On-Board Diagnostics (OBD)
Abstract:The continuously stringent emission regulations call for the adaptation of the Selective Catalytic Reduction (SCR) system by many diesel manufacturers. In order to show the EPA the latest emission compliance, the sensors are required to be installed at upstream and downstream of the SCR. As a result, the reduction efficiency is also required to be monitored by the On-Board Diagnostics (OBD) regulations. Specifically, a diagnostic algorithm is required to detect and isolate the SCR system faults that may cause emission violation. In this research, two model-based fault detection and isolation algorithms are developed to detect and isolate the dosing fault and the outlet NOx sensor fault for the SCR system. The dosing fault is treated as an actuator additive fault, while the outlet NOx sensor drift and/or offset fault is treated as a sensor additive fault. First, a 0-D SCR nonlinear dynamic model was developed to facilitate the model-based approaches. A parity equation residual generator was designed based on the linearized SCR model and the fault transfer function matrix. Then, a sliding mode observer based residual generator is designed directly based on the nonlinear model. The two diagnostic algorithms are then implemented in the Matlab/Simulink environment for validation. A high fidelity nonlinear 1-D SCR model is used to generate system outputs and to simulate the plant. The simulation results show that the two model-based fault diagnosis approaches are capable of detecting and isolating the outlet NOx sensor and dosing faults in real time.
Issue Date:2014-09-16
Rights Information:Copyright 2014 Rui Chen
Date Available in IDEALS:2014-09-16
Date Deposited:2014-08

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