A mechanical component can break due to repeated load cycling, even if these loads remain well below the component’s regular static strength. In a simplified fashion, a component’s fatigue life depends on the loads that it has to endure during its service life, as well as its fatigue strength to resist the formation of cracks. Since both of these factors can be considered as random variables, the time until a fatigue-induced rupture occurs can be considered as a random variable as well. Airworthiness regulations require that aircraft manufacturers show by numerical analysis that the probability that a fatigue failure occurs during a critical part’s maximum allowable service life does not exceed a specified probability.

Fatigue life is a random variable. The reliability of a conservative fatigue life prediction for a component in the helicopter dynamic system thus needs to be substantiated. A standard analytical substantiation method uses averaged manoeuvre loads instead of seeing manoeuvre loads as a random variable whose distribution is estimated with limited precision. This simplification may lead to inaccuracies. A new simulation-based method is developed to conservatively predict fatigue life while also accounting for the full random distribution and uncertainty of manoeuvre loads. Both methods fully account for uncertain fatigue strength but assume that the mission profile is known or can at least be conservatively estimated. Simulations under synthetic but realistic engineering conditions demonstrate that both methods may be used for accurate substantiation of conservative fatigue life predictions. The simulations also demonstrate that, under the tested conditions, uncertainties from manoeuvre loads may be neglected in fatigue life substantiations as the resulting error is not significant with respect to uncertainties in component fatigue strength.

Rotorcraft have many safety-critical components that are subject to repetitive loading and whose predicted fatigue life is substantiated by a statistically derived reduction factor for a working S-N curve. For cases where the statistical analysis must be performed based on very few samples, or even none at all, commonly used frequentist statistics are demonstrated to be inadequate. As an alternative, the value of Bayesian statistics is exemplified using real data and a simplified fatigue life substantiation model from industry. The case study exemplifies how well-established industry practise can be substantiated by traceable and explicit statistical analysis. It is thus exemplified how Bayesian statistics can reduce the number of critical engineering assumptions in fatigue life substantiation, or make them more explicit and traceable