University of Hertfordshire

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Original languageEnglish
Publication statusPublished - 8 May 2019
Event2nd International Conference on Advances in Aerospace Structures, Systems & Technology - Croydon, United Kingdom
Duration: 8 May 20199 May 2019
https://www.asranet.co.uk/Content/Files/AASST%202019.pdf

Conference

Conference2nd International Conference on Advances in Aerospace Structures, Systems & Technology
Abbreviated titleAASST 2019
CountryUnited Kingdom
CityCroydon
Period8/05/199/05/19
Internet address

Abstract

There is great interest in the benefits of Structural Health and Usage Monitoring in the Aerospace Industry both from a safety point of view and because of the possibility of extending the life of aerospace structural components. Although fail-safe and damage tolerance approaches to design are extensively used and have great advantages, there are never the less components and circumstances where a safe life approach remains appropriate. This leads to an approach to fatigue clearance whereby a component will be taken out of service after a certain number of hours usage irrespective of the environment it has experienced having been cleared based on very conservative loading assumptions. If the actual loads experienced by critical parts of a structure can be derived from a Structural Health and Usage Monitoring System (SHUMS), this then leads to the possibility of extending the time for which the component can remain in service with consequent cost savings. In this paper, a number of fundamental approaches to loads prediction using data available from a Structural Health and Usage Monitoring Systems are reviewed, with the particular application in mind being that of an air-carried guided weapon. Approaches considered will include time-domain and frequency-domain based methods making use of a structural model, together with machine learning based approaches. Their different strengths, weaknesses and pitfalls will be highlighted together with ways to overcome them. Practical aspects of their possible implementation will also be addressed.

ID: 17124920