Real time supervision of diesel engine injection with RBF-based neural networks

This paper deals with real time supervision of turbo Diesel combustion engines based on acquisition and evaluation of cylinder pressure signals. The idea is to subtract a "reconstructed" towed pressure from the fired pressure signal which results in a contrast amplification. From the resulting difference pressure signal, significant features are extracted. By means of a RBF (radial basis functions) neural network, these features are mapped on injected fuel mass and injection angle. By comparison with the corresponding specified input signals, injection faults can de detected. With the presented method, it becomes possible to separate fuel mass problems from injection angle failures. The concept has been implemented on a dynamic engine test stand.