A new on-line fault detection and isolation (FDI) scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in this paper. The neural classifier is adaptive to cope with the significant parameter uncertainty, disturbances, and environment changes. The developed scheme is capable of diagnosing faults in on-line mode and can be directly implemented in an on-board diagnosis system (hardware). The robustness of the FDI for the closed-loop system with crankshaft speed feedback is investigated by testing it for a wide range of operational modes including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all these changes occurring at the same time. The evaluations are performed using a mean value engine model (MVEM), which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.
Fault detection and isolation have become one of the most important aspects of automobile design. A fault detection (FD) scheme is developed for automotive engines in this paper. The method uses an independent Radial Basis Function (RBF) Neural Network model to model engine dynamics, and the modelling errors are used to form the basis for residual generation. A dependent RBFNN model is a model which uses output data of a plant as a target output then use it to train the neural network, while, The independent RBFNN model is a higher accuracy than the dependent model and the errors can be detected by this model, this is because this model does not dependent on the output of the plant and it will use its output as a target, so if any faults in the plant will be not effect in the model and this faults will be detected easily and clearly. The method is developed and the performance assessed using the engine benchmark, the Mean Value Engine Model (MVEM) with Matlab/Simulink. Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults considered are 10-20% change superimposed on the outputs of manifold pressure, temperature and crankshaft speed sensors; one component fault considered is air leakage in intake manifold and Exhaust Gas Recycle (EGR); the actuator fault considered is the malfunction of fuel injector. The simulation results showed that all the simulated faults can be clearly detected in the dynamic condition throughout the operating range.
This paper presents a new method for on-board fault diagnosis for the air path of spark ignition (SI) engines. The method uses a radial basis function (RBF) neural network to classify predefined possible faults from engine measurements, reporting fault occurrence as well as the type and size of a fault. After diagnosing faults in each sample interval, the weights and widths of the RBF fault classifier are updated with the measurements and appropriately selected target outputs. Consequently, the network can adapt to the time-varying dynamics of the engine and environment change so that the false alarm rate is greatly reduced and the required network size is also reduced. The developed scheme is assessed with various faults simulated on a mean value engine benchmark model and compared with a fixed-parameter RBF classifier. Simulation results demonstrate the effectiveness of the proposed method.
Fault detection and isolation have become one of the most important aspects of automobile design. A new fault detection and isolation scheme is developed for automotive engines in this paper. The method uses an independent radial basis function neural network model to model engine dynamics, and the modelling errors are used to form the basis for residual generation. Furthermore, another radial basis function network is used as a fault classifier to isolate 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 with Matlab/Simulink. Five faults have been simulated on the mean value engine model, including 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; the actuator fault considered is the malfunction of 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.
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