THESIS
2022
1 online resource (xii, 173 pages) : illustrations (some color)
Abstract
This body of work encompasses a unified treatment of uncertainty associated with multiaxial
fatigue damage accumulation. The first part of this study presents a framework for
fatigue monitoring on linear components of a structure subjected to unknown loads, using
a limited number of output-only vibration measurements. It exploits virtual sensing techniques
to estimate the dynamical responses, including virtual multiaxial strain/stress time
histories. Joint input-state estimation algorithms are applied to predict the dynamical
response of the structural components. The resulting virtual multiaxial stress histories are
used to compute the fatigue load cycles and corresponding fatigue damage. The proposed
procedure can be adopted over a monitoring period to produce a global fatigue damage...[
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This body of work encompasses a unified treatment of uncertainty associated with multiaxial
fatigue damage accumulation. The first part of this study presents a framework for
fatigue monitoring on linear components of a structure subjected to unknown loads, using
a limited number of output-only vibration measurements. It exploits virtual sensing techniques
to estimate the dynamical responses, including virtual multiaxial strain/stress time
histories. Joint input-state estimation algorithms are applied to predict the dynamical
response of the structural components. The resulting virtual multiaxial stress histories are
used to compute the fatigue load cycles and corresponding fatigue damage. The proposed
procedure can be adopted over a monitoring period to produce a global fatigue damage
accumulation map from limited vibration data. The next part of this study proposes a
Bayesian framework to re-formulate a multiaxial fatigue model and produce probabilistic
stress-life fatigue curves from experimental data. The framework is employed to identify
the experimentally-driven parameters that govern the multiaxial fatigue model, in the
form of probability distributions. Classical and hierarchical Bayesian inference strategies
are presented, accompanied by rigorous analytical expressions for calculating the joint
posterior distributions necessary for in-field implementation. This probabilistic treatment
makes the existing fatigue models suitable for exercising uncertainty propagation
for reliability analysis and design purposes. Lastly, a framework for multiaxial fatigue
reliability assessment is proposed to incorporate uncertainty from diverse sources. It thus
provides realistic fatigue damage accumulation information for a component based on the
actual operational conditions of a structure.
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