James Wilson

James Wilson
  • Doctor of Philosophy
  • Research Associate at The University of Sheffield

About

8
Publications
310
Reads
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9
Citations
Current institution
The University of Sheffield
Current position
  • Research Associate
Additional affiliations
August 2017 - August 2018
RJM International
Position
  • Engineer
Education
September 2019 - July 2022
The University of Sheffield
Field of study
  • Verification and validation of physics-based models for structural health monitoring
September 2014 - July 2019
The University of Sheffield
Field of study
  • Mechanical Engineering with a Year in Industry

Publications

Publications (8)
Article
The use of measured vibration data from structures has a long history of enabling the development of methods for inference and monitoring. In particular, applications based on system identification and structural health monitoring have risen to prominence over recent decades and promise significant benefits when implemented in practice. However, si...
Preprint
Full-text available
The use of measured vibration data from structures has a long history of enabling the development of methods for inference and monitoring. In particular, applications based on system identification and structural health monitoring have risen to prominence over recent decades and promise significant benefits when implemented in practice. However, si...
Article
Full-text available
This paper presents a demonstrative application of a forward model-driven approach to structural health monitoring (SHM), incorporating hierarchical validation methods. A key tenet of the approach is that an SHM system can be constructed that is capable of diagnosing damage at the full system level, without full system damage-state data having been...
Chapter
Structural health monitoring has seen significant progress in recent decades and offers major potential benefits in terms of life-cycle management of engineering infrastructure compared to traditional monitoring and maintenance methods. However, many challenges remain, including the lack of availability of sufficient damage-state data from structur...
Chapter
Despite the success of data-based methods in structural health monitoring (SHM), these approaches often suffer from a lack of training data, which can be difficult to acquire for several reasons: damage-state data acquisition can be infeasible, structures may be unique and only tested in situ, sensor placement can cause issues, certain structures c...
Conference Paper
Structural health monitoring (SHM) has been limited by a lack of available damage-state data, leading to various mitigating strategies. Using numerical models to generate this data – as in forward model-driven SHM (FMD-SHM) – has potential, however validation remains an issue. A hierarchical strategy allows assembly models to be validated at sub-as...

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