Resumen
A very fast reduced order model is developed to monitor aeroengines condition (defining their degradation from a baseline state) in real time, by using synthetic data collected in specific sensors. This reduced model is constructed by applying higher-order singular value decomposition plus interpolation to appropriate data, organized in tensor form. Such data are obtained by means of an engine model that takes the engine physics into account. Thus, the method synergically combines the advantages of data-driven (fast online operation) and model-based (the engine physics is accounted for) condition monitoring methods. Using this reduced order model as surrogate of the engine model, two gradient-like condition monitoring tools are constructed. The first tool is extremely fast and able to precisely compute the turbine inlet temperature ?on the fly?, which is a paramount parameter for the engine performance, operation, and maintenance, and can only be roughly estimated by the engine instrumentation in civil aviation. The second tool is not as fast (but still reasonably inexpensive) and precisely computes both the engine degradation and the turbine inlet temperature at which sensors data have been acquired. These tools are robust in connection with random noise added to the sensor data and can be straightforwardly applied to other mechanical systems.