Extreme value theory-based structural health prognosis method using reduced sensor data

Abstract

This paper presents an extreme value theory (EVT)-based structural health prognosis method that can be used for estimating the quantile values of remaining useful life (RUL) of monitored structure with reduced sensor data. Massive sensor data generated from online structural health monitoring system can be utilised to provide more refined prognosis results such as statistical distribution of RULs. By using the moment estimator from EVT, only a small portion of the full sensor data-set is actually used for estimating the quantile values of the RUL. This can considerably cut the computing time required for structural health prognosis. As a requirement for implementing the EVT-based prognosis, monotonicity relation between damage index (either measured or derived from sensor data) and RUL values has to be satisfied though. Common prognosis problems in civil engineering include fatigue cracking in steel structures and steel rebar corrosion in reinforced concrete structures. To illustrate the EVT-based prognosis, the monotonicity condition is shown for the selected degradation models in two case studies involving fatigue life estimation and pitting corrosion life of steel reinforcing rebar, respectively. The results show that EVT-based structural health prognosis method is computationally efficient without loss of much accuracy.