Monitoring of Cylinder Pressures and Vibration in Internal Combustion Engine



We focus on the detection of incipient faults in an internal combustion engine using a minimum number of sensory information.  Inducing several faults in a 4 stroke diesel engine, cylinder pressure (P) and vibration (V) data are acquired.  Two sets of artificial neural nets (ANN) are trained separately, using features from the pressure and vibration data.   Both sets of nets show very good fault detection capabilities, thus demonstrating an alternative to the multi-sensory approach commonly  adopted  in  fault  diagnosis.    In  a separate study, P and V are fused together at the signal level and then used to train another set of ANNs which is shown to exhibit better reliability than either system.  In the final study, the outputs of the 3 systems (P, V and  fused  P  and  V),  are  combined  together  in  a  majority  voting  system  which  outperforms  all  of  its constituents in its diagnostic abilities, successfully identifying 2854 out of 3000 test cases with a confidence level of 90%.

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