This paper presents a methodology to monitor the fatigue life of aerospace structures and hence the remaining allowable fatigue life. In fatigue clearance, conservative load assumptions are made. However, in reality, a structure may see much lower loads and so would be usable for much longer. An example ofthis is air carried guided missiles. In the UK, missiles must be decommissioned after a period of carriage. The implementation of a system that can monitor the usage of a missile during its time in service is advantageous to the military customer and provides a competitive advantage for the missile manufacture inexport markets where reduced through-life costs, longer in-service lives and increased safety are desired. The proposed methodology provides a means to monitor the service life of a missile. This paper describes how machine learning algorithms can be used with accelerometers to determine loads on a missile structure which would then be used to predict how long the missile has left in service.
|Title of host publication||Proceedings of ISMA 2018 International Conference on Noise and Vibration Engineering|
|Editors||W Desmet, B Pluymers, D Moens, W Rottiers|
|Number of pages||12|
|Publication status||Published - 17 Sept 2018|