Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.822224
Title: Probabilistic approach to structural integrity assessment of fatigue damage using permanently installed monitoring systems
Author: Leung, Siu Hey Michael
ISNI:       0000 0005 0287 3507
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2020
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Abstract:
This thesis aims to develop strategies for integrating frequent onload data obtained from permanently installed monitoring systems with probabilistic structural integrity methods in order to produce real-time, uncertainty-quantified diagnostics and prognostics for fatigue damage. The proposed strategy is broadly divided into two phases: defect detection, and defect growth monitoring. For the defect detection phase, a framework for evaluating the detection capabilities of PIMS is first proposed. This is essential to qualifying PIMS for industrial applications, and forms the basis of quantifying its value for structural integrity assessment. The framework is then utilised to address the well-recognised compromise between area coverage and sensitivity of PIMS. By combining information on the spatial sensitivity of PIMS and the spatial uncertainty of defect location, the detection capabilities of specific combinations of monitoring systems and components can be quantitatively compared. A novel approach to incorporate measurements from PIMS into structural integrity assessment is subsequently proposed. The ability of PIMS to recursively eliminate the possibility of there being substantial damage in the monitored component is demonstrated, which proves to be an effective way of maintaining confidence in its structural integrity. This framework will therefore help promote the adoption of PIMS for damage detection in suitable engineering applications. For the defect growth monitoring phase, the ability of PIMS to produce accurate rate measurements is exploited to perform remnant life predictions using the Failure Forecast Method (FFM). A statistical analysis comparing the conventional inspection-based approach to the FFM approach is performed, demonstrating the ability of the FFM approach to estimate more accurately the remnant life of the monitored component. A novel method for using the FFM under non-constant amplitude loading conditions is also developed and validated. This enables the use of the FFM in more complex loading conditions, thereby advancing its potential uses in real-life applications.
Supervisor: Corcoran, Joseph ; Cawley, Peter Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.822224  DOI:
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