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This paper reports on non-linear principal component analysis for fault detection on an internal combustion (IC) engine. An auto-associative neural network (AANN) model is built from transient engine data collected under varying atmospheric conditions. The experimental data used for modelling was collected for two different drive cycles, the Identification Cycle and the New European Drive Cycle. The key issue here is to decide which data should be used for training the neural network to produce good fault detection generalisation under different atmospheric conditions and with a different drive cycle. This is achieved successfully, with the Q monitoring statistic indicating an absence of unwanted false alarms under fault-free operation, along with successful detection of air leaks of varying magnitude in the inlet manifold.

Transient IC Engine Monitoring Under Temperature Changes Using an AANN