Statistical Performance Assessment of an NDE-Based SHM-DP Methodology for the Remaining Fatigue Life Prediction of Monitored Structural Components and Systems

CATEGORY: REMAINING USEFUL LIFE
Statistical Performance Assessment of an NDE-Based SHM-DP Methodology for the Remaining Fatigue Life Prediction of Monitored Structural Components and Systems

Type	Journal Article
Author	M. Gobbato
Author	J. P. Conte
URL	http://ieeexplore.ieee.org/document/7447672/
Volume	104
Issue	8
Pages	1575-1588
Publication	Proceedings of the IEEE
Date	August 2016
Abstract	Integrated structural health monitoring and damage prognosis (SHM-DP) methodologies, coupled with sensor-based nondestructive evaluation (NDE) techniques, are important for the near-real-time condition assessment (i.e., SHM) and future performance predictions (i.e., DP) of aging mechanical systems, civil structures, and infrastructure networks, as well as automotive, naval, and aerospace vehicles. A successful SHM-DP strategy, capable of identifying all critical damage mechanisms while accounting for all relevant sources of uncertainty, can be used as an advanced tool to manage the life-cycle of the monitored system, recursively forecast its remaining useful life (RUL), and reduce the overall ownership cost through dynamic reliability-based inspection and maintenance (RBIM) plans, system downtime minimization, catastrophic failure prevention, and potential RUL extension. Fatigue damage propagation is one of the most critical and unpredictable deterioration processes for a large variety of structural and mechanical systems that are subjected repeatedly to cyclic and/or random operational loading during their service life. Within this limited scope, the authors developed a comprehensive NDE-based SHM-DP framework for recursively predicting the time-varying system reliability and the remaining fatigue life (RFL) of monitored systems subjected to deterioration by multi-site fatigue damage propagation. They offer a brief overview of the proposed framework and then use a set of experimental fatigue test data to perform a thorough statistical performance assessment of the developed methodology at the local reliability component level  including NDE detectability and measurement uncertainty as well as both load and model parameter uncertainty.