Verification of probabilistic risk assessment method AMETA for aircraft fatigue life management

Abstract

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.