It is generally known that an engine component will accumulate damage (life usage) during its lifetime of use in a harsh operating environment. The commonly used cycle count for engine component usage monitoring has an inherent range of uncertainty that can be overly costly or potentially less safe from an operational standpoint. This paper describes an approach to quantify the effects of engine operating parameter uncertainties on the thermomechanical fatigue (TMF) life of a selected engine part. A closed-loop engine simulation with a TMF life model is used to calculate the life consumption of different mission cycles. A Monte Carlo simulation approach is used to generate the statistical life usage profile for different operating assumptions. The probabilities of failure of different operating conditions are compared to illustrate the importance of the engine component life calculation using sensor information. The results of this study clearly show that a sensor-based life cycle calculation can greatly reduce the risk of component failure as well as extend on-wing component life by avoiding unnecessary maintenance actions.
[1]
Ronald L. Wasserstein,et al.
Monte Carlo: Concepts, Algorithms, and Applications
,
1997
.
[2]
V. K. Arya,et al.
Application of a Thermal Fatigue Life Prediction Model to High-Temperature Aerospace Alloys B1900 + Hf and Haynes 188
,
1992
.
[3]
William J. Wilson,et al.
Statistical methods (2. ed.)
,
2003
.
[4]
W. Weibull.
A Statistical Distribution Function of Wide Applicability
,
1951
.
[5]
Christian P. Robert,et al.
Monte Carlo Statistical Methods
,
2005,
Springer Texts in Statistics.
[6]
S. S. Manson,et al.
Application of a method of estimating high- temperature low-cycle fatigue behavior of materials.
,
1967
.
[7]
S. Manson.
Fatigue: A complex subject—Some simple approximations
,
1965
.