Prediction Capability Assessment of Data Driven Prognostic Methods for Railway Applications
In the development of Prognostics and Health Management (PHM) for industrial applications, the question of which predictive method to use is fundamental. The choice is typically driven by the data and/or the physics based models available, and the cost benefit considerations related to PHM implementation, wherein prediction capability plays an important role. By prediction capability of a prognostic method we refer to its ability to provide trustable predictions of the Remaining Useful Life (RUL) of a component or system, with the characteristics required by the given application. A set of Prognostic Performance Indicators (PPIs) is used to guide the choice of the method to be implemented. These PPIs measure different characteristics of a prognostic method and need to be aggregated to enable a final choice of prognostic method, based on its overall performance. We propose an aggregation strategy to identify the prognostic method with the best compromise performance on all PPIs. The strategy is exemplified on a case study with real data taken from industry, whose structure is general and, therefore, applicable to railway industry.