PHM functions maturation

Abstract

Snecma has been developing Prognostic and Health Monitoring (PHM) functions to monitor different sub-systems of an aircraft engine. To gain maturity and to take more accurate decisions, algorithms need a reality check on the significance of their results. Algorithms have been deployed on in service fleets of engines, getting access to large amounts of data. Nevertheless, probabilities of failure are very low. Thus there are not enough degradation cases collected to compute with accuracy performance metrics, such as Probability of False Alarm (PFA) and Probability of Detection (PoD), for each algorithm. To address this issue, healthy indicators distributions are used to set detection alarm thresholds. Those thresholds are first checked on healthy data and on rare degradations. Algorithms shall indeed raise no alarm on healthy cases and detect all rare degradations. This allows to alarm the operator only when a health indicator is changing. On the base of following observed failure mechanisms, simulations can help to compute with accuracy performance metrics.