Supervised and unsupervised condition monitoring of non-stationary acoustic emission signals

We are pursuing a system that monitors the engine condition under multiple load settings, i.e. under non-stationary operating conditions. The running speed when data acquired under simulated marine conditions (different load settings on the propeller curve) was in the range from approximately 70 to 125 rotations per minute; furthermore the running speed was stable within periods of fixed load. Electronically controlled engines can change the angular timing of certain events, such as fuel injection in order to optimize its performance. However, this behaviour makes it difficult to detect condition changes across load changes. In this paper we approach this load interpolation problem with supervised and unsupervised learning, i.e. model with normal and fault examples and normal examples only, respectively. We apply non-linear methods for the learning of engine condition changes. Both approaches perform well, which indicates that unsupervised models, modelled without faulty data, may be used for accurate condition monitoring.