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Enhanced Recursive Probabilistic Integration Method for Probabilistic Fatigue Life Management Using Structural Health Monitoring

T. CHEN, M. SHIAO

Abstract


An enhanced recursive probabilistic integration (ERPI) algorithm was developed based on the recursive probabilistic integration (RPI) method for damage tolerance analysis of fatigue life using structural health monitoring (SHM). RPI is an efficient probabilistic method which is an event-tree based probabilistic framework using a baseline crack growth histories repeatedly for various maintenance plans under various uncertainties such as variability in material properties, initial flaw size, random processes of the fight loading, inspection reliability and inspection correlation for SHM monitoring systems. ERPI inherits the probabilistic framework and computational efficiency of RPI method. However, it uses a forward recursive numerical scheme to further improve RPI’s computational efficiency and also greatly enhance RPI’s capability regarding variable inspection intervals such that nonrepaired and repaired components can have different inspection schedules. In this study, Monte Carlo simulations (MCS) were conducted to verify and demonstrate the ERPI algorithm for flight life plans with variable inspection intervals under various flight conditions subject to various uncertainties. The study also demonstrated the efficiency of ERPI that the ratio of the CPU time using MCS to that of ERPI is 700:1.

doi: 10.12783/SHM2015/289


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