A methodology using health and usage monitoring system data for payload life prediction

Andrew Lewis, Philip Nalliah, Colin Lomax, Chris Hawkins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publicationProceedings of ISMA 2018 International Conference on Noise and Vibration Engineering
EditorsW Desmet, B Pluymers, D Moens, W Rottiers
PublisherKU Leuven
Number of pages12
Publication statusPublished - 17 Sept 2018


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