A new gradient-like methodology has been developed for aeroengine diagnosis, determining the engine health condition, which is defined by the engine degradation from an undegraded state and uses measurements at various sensors distributed along the engine. The developed tools are able to accurately compute, not only the engine degradation, but also the turbine inlet temperature, which is very important and novel in the field. The quality of a given sensors set is evaluated, and a method is developed to guide in the improvement of deficient sensors sets. The methodology is tested in a representative two-spool turbofan engine, obtaining consistent results in a computationally inexpensive way. Moreover, results are robust in connection with the random noise added to the sensors data.

Nowadays, there is an ever growing interest for gas turbine and aeroengines prognostics. The capability to assess not only the current state of an asset, but also to be able to predict its remaining useful life (RUL), and hence to perform condition-based maintenance (CBM) —if, and only when, it is needed— can represent a huge deal in the manufacturer profits. Against the plethora of data-driven methods that have arisen in the past few years, there is still some knowledge to be gained in terms of understanding the underlying phenomenology of engine degradation. In fact, it is certainly a non-trivial problem, to realize what has happened to the rotating components of an engine just by observing the pressure being measured by certain sensor rise, or some other temperature measured along the main gas-path decrease its value. In this regard, model-based approaches —and, in particular, gas path analysis (GPA)— can assist us in gaining such knowledge. In this paper, a non-linear GPA technique is revisited, introducing some novelties to the solver, and making use of current computational methods and resources, to establish a solid ‘foundation’ that will serve as the basis for further research.

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.

A reduced order model is developed to monitor aeroengines condition (defining their degradation from a baseline state) in real-time, by using 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 using a detailed 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 `on the fly’ the turbine inlet temperature, 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 so 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 sensors data and can be straight forwardly applied to other mechanical systems.