A probabilistic risk assessment method to assess the failure possibilities of aircraft fatigue critical components due to fatigue damage initiation and propagation, as well as the effect of complex maintenance scenarios throughout the aircraft’s service life (including multiple repair types and various nondestructive inspection (NDI) techniques), needs to be developed for aircraft fatigue life management. The traditional Monte Carlo simulation (MCS) offers the most robust and reliable solution; however, MCS is time consuming and unable to support prompt risk decisions. To relieve the computational burden, a novel probabilistic method—AMETA (Aircraft Maintenance Event Tree Analysis)—was developed, which combines the generality of random simulations with the efficiency of analytical probabilistic methods. AMETA consists of a fatigue maintenance event tree and a probabilistic algorithm comprising a set of probabilistic equations. AMETA systematically transforms a complex random maintenance pattern requiring a large number (in the order of billions) of MCSs to more logical and manageable fatigue paths represented by a finite set of probabilistic events to achieve the required computational accuracy and efficiency. Furthermore, the Importance Sampling Method (ISM) can be used for efficiency improvement. In this paper, the accuracy, efficiency and robustness of AMETA are verified and demonstrated. A procedure was provided to select the most suitable sampling functions for ISM. It is found that AMETA is several orders of magnitude more efficient than MCS for the same level of accuracy.
This paper verified a generic and efficient assessment concept for probabilistic fatigue life management. The concept is developed based on an integration of damage tolerance methodology, simulations methods1, 2, and a probabilistic algorithm RPI (recursive probability integration)3-9 considering maintenance for damage tolerance and risk-based fatigue life management. RPI is an efficient semi-analytical probabilistic method for risk assessment subjected to various uncertainties such as the variability in material properties including crack growth rate, initial flaw size, repair quality, random process modeling of flight loads for failure analysis, and inspection reliability represented by probability of detection (POD). In addition, unlike traditional Monte Carlo simulations (MCS) which requires a rerun of MCS when maintenance plan is changed, RPI can repeatedly use a small set of baseline random crack growth histories excluding maintenance related parameters from a single MCS for various maintenance plans. In order to fully appreciate the RPI method, a verification procedure was performed. In this study, MC simulations in the orders of several hundred billions were conducted for various flight conditions, material properties, and inspection scheduling, POD and repair/replacement strategies. Since the MC simulations are time-consuming methods, the simulations were conducted parallelly on DoD High Performance Computers (HPC) using a specialized random number generator for parallel computing. The study has shown that RPI method is several orders of magnitude more efficient than traditional Monte Carlo simulations.
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