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Title: Intelligent fault diagnosis for automotive engines
Author: Hamad, Adnan
Awarding Body: Liverpool John Moores University
Current Institution: Liverpool John Moores University
Date of Award: 2012
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Fault detection and isolation (FDI) has become one of the most important aspects of automobile design. In this thesis, a new fault detection and isolation approach is developed for automotive engines. The method uses an independent radial basis function neural network model to model engine dynamics, and the modelling errors are used to from the basis for residual generation. Furthermore, another radial basis function neural network is used as a fault classifier to isolate an occurred fault from other possible faults in the system by classifying fault characteristics embedded in the modelling errors. The performance of the developed scheme is assessed using an engine benchmark, the Mean Value Engine Model CMVEM), with Matlab/Simulink. Five faults have been simulated on the MVEM: three sensor faults, one component fault and one actuator fault. The three sensor faults considered are 10~20% changes superimposed on the measured outputs of manifold pressure, manifold temperature and crankshaft speed sensors; the component fault considered is air leakage in the intake manifold; and the actuator fault considered is the malfunction of the fuel injector. The simulation results show that all the simulated faults can be clearly detected and isolated in dynamic conditions throughout the engine operating range. Furthermore, in order to reflect the real state for an automotive engine, the FDI method is evaluated for the MVEM system under closed-loop control with air fuel ratio control. An independent radial basis function (RBF) neural network model is used to model engine dynamics using random amplitude signals (RAS) throttle angle as an input.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available