Condition-based maintenance programs for modern helicopters rely on algorithmic techniques to estimate the useful life remaining for life-limited components. Regime-recognition-based condition-based maintenance programs involve a regime recognition step and a damage estimation step in which damage is calculated based on the identified regimes. Recently, new probabilistic regime recognition algorithms have been developed that produce probability distributions over the regimes, rather than deterministic regime classifications. However, to date, there has been no method to convert regime distributions to damage estimates. This paper proposes a technique to compute a probability distribution over the fatigue damage for each life-limited component directly from the regime probability distributions. The method treats the incurred damage at a given time as a random variable and accumulates the total damage incurred as a sum of random variables. The damage distribution at each time is computed from the regime distribution and the regime damage rates. A primary advantage of the approach is that it captures uncertainty in the regime recognition process by treating damage as a random variable rather than a deterministic value. Simulation results illustrate the benefit of the probabilistic approach over a deterministic method, particularly for flights where there is significant uncertainty in the flown regimes.

现代直升机的基于状态的维护计划依靠算法技术来估计寿命有限的部件的剩余使用寿命。基于状态识别的基于状态的维护程序包括状态识别步骤和损坏估计步骤，其中基于识别的状态计算损坏。最近，已经开发了新的概率状态识别算法，该算法在状态上产生概率分布，而不是确定性状态分类。然而，迄今为止，还没有将状态分布转换为损害估计的方法。本文提出了一种技术，可以直接从状态概率分布中计算每个寿命有限部件的疲劳损伤概率分布。该方法将给定时间发生的损害视为随机变量，并将发生的总损害累积为随机变量的总和。每次的损伤分布由状态分布和状态损伤率计算。该方法的一个主要优点是它通过将损坏视为随机变量而不是确定性值来捕获状态识别过程中的不确定性。仿真结果表明概率方法优于确定性方法，特别是对于飞行状态存在重大不确定性的飞行。该方法的一个主要优点是它通过将损坏视为随机变量而不是确定性值来捕获状态识别过程中的不确定性。仿真结果表明概率方法优于确定性方法，特别是对于飞行状态存在重大不确定性的飞行。该方法的一个主要优点是它通过将损坏视为随机变量而不是确定性值来捕获状态识别过程中的不确定性。仿真结果表明概率方法优于确定性方法，特别是对于飞行状态存在重大不确定性的飞行。