The scenario approach for data-driven prognostics

Prognostics is the process of forecasting the time-to-failure or the time-to-alarm of an industrial item using degradation models. Data-driven approaches to prognostics employ regression models ft on condition indicators computed from raw run-to-failure data to extrapolate the degradation behaviour of the item. The development of a reliable data-driven degradation model typically requires many run-to-failure acquisitions to understand the degrading behavior. Such experimental tests are destructive and expensive for items manufacturers. Thus, decreasing the number of run-to-failure experiments is key in reducing predictive maintenance costs. In this work, focusing on time-to-alarm prediction to anticipate items breakdown, we propose a data-driven method based on the scenario approach to characterise the degradation behaviour of an industrial item in certain operative conditions using only one run-to-failure experiment, updating the time-to-alarm prediction only when needed. The scenario approach gives probabilistic guarantees on the time-to-alarm predictions.