Verification of Probabilistic Risk Assessment Method AMETA for Aircraft Fatigue Life Management

摘要

A probabilistic risk assessment method to assess the failure possibilities of aircraft fatigue critical components due tofatigue damage initiation and propagation, as well as the effect of complex maintenance scenarios throughout the aircraft’sservice life (including multiple repair types and various nondestructive inspection (NDI) techniques), needs to bedeveloped for aircraft fatigue life management. The traditional Monte Carlo simulation (MCS) offers the most robust andreliable solution; however, MCS is time consuming and unable to support prompt risk decisions. To relieve thecomputational burden, a novel probabilistic method-AMETA (Aircraft Maintenance Event Tree Analysis)-wasdeveloped, 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 probabilisticequations. AMETA systematically transforms a complex random maintenance pattern requiring a large number (in theorder of billions) of MCSs to more logical and manageable fatigue paths represented by a finite set of probabilistic eventsto achieve the required computational accuracy and efficiency. Furthermore, the Importance Sampling Method (ISM) canbe used for efficiency improvement. In this paper, the accuracy, efficiency and robustness of AMETA are verified anddemonstrated. A procedure was provided to select the most suitable sampling functions for ISM. It is found that AMETAis several orders of magnitude more efficient than MCS for the same level of accuracy.