Generic approach towards a diagnostic and prognostic data suitability assessment

Spacecraft and aircraft systems collect health-related data continuously, which can give an indication of the systems' health status. While they rarely occur, the repercussions of such system anomalies, faults, or failures can be severe, safety-critical and costly. Therefore, the data are used to anticipate any kind of anomalous behaviour or predict remaining useful life. Most of the existing approaches are tailored to one specific system. They do not provide a high degree of flexibility and often cannot be adaptively used on different systems. This can lead to years of research, knowledge and expertise being put in the implementation of prognostics models without the capacity to estimate remaining useful life of systems either because of lack of data or data quality or simply because failure behaviour cannot be captured by data-driven models. To overcome this, in this thesis we present an adaptive diagnostic and prognostic framework which can be applied to different systems while providing a way to assess whether or not it makes sense to put more time into the development of diagnostic or prognostic models for a system. In addition, we apply this framework to both, an aircraft and satellite data set to characterize the impact of metrics on the choice of diagnostic and prognostic methodologies. We highlight considerations for suitability assessment of systems data towards prognostics and diagnostics and show that the choice of optimization metric has a big impact on the output of the generic diagnostic and prognostic framework. The results indicate that such a framework can gives directions for practitioners as to whether or not it makes sense to invest time and money in the development of diagnostic or prognostic systems based on the available system data.