[Reliability Modeling of Complex systems using both stochastic and heat signal capture in Thermo graphic captures with a case study on the Palm Oil mill]

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Acknowledgement

I would take this opportunity to thank my research supervisor, family and friends for their support and guidance without which this research would not have been possible.

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I, [type your full first names and surname here], declare that the contents of this dissertation/thesis represent my own unaided work, and that the dissertation/thesis has not previously been submitted for academic examination towards any qualification. Furthermore, it represents my own opinions and not necessarily those of the University.

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ABSTRACT

In many engineering applications, it is a formidable task to construct mathematical models that are expected to produce accurate predictions of the behavior of a system of interest. During the construction of such predictive models, errors due to imperfect modeling and uncertainties due to incomplete information about the system and its environment (e.g., input or excitation) always exist and can be accounted for appropriately by using probability logic. To assess the system performance subjected to dynamic excitations, a stochastic system analysis considering all the uncertainties involved has to be performed. In engineering, evaluating the robust failure probability (or its complement, robust reliability) of the system is a very important part of such stochastic system analysis. The word 'robust' is used because all uncertainties, including those due to modeling of the system, are taken into account during the system analysis, while the word 'failure' is used to refer to unacceptable behavior or unsatisfactory performance of the system output(s). Whenever possible, the system (or subsystem) output (or maybe input as well) should be measured to update models for the system so that a more robust evaluation of the system performance can be obtained. In this thesis, the focus is on stochastic system analysis, model and reliability updating of complex systems, with special attention to complex dynamic systems which can have high-dimensional uncertainties, which are known to be a very challenging problem. Here, full Bayesian model updating approach is adopted to provide a robust and rigorous framework for these applications due to its ability to characterize modeling uncertainties associated with the underlying system and to its exclusive foundation on the probability axioms.First, model updating of a complex system which can have high-dimensional uncertainties within a stochastic system model class is considered. To solve the challenging computational problems, stochastic simulation methods, which are reliable and robust to problem complexity, are proposed. The Hybrid Monte Carlo method is investigated and it is shown how this method can be used to solve Bayesian model updating problems of complex dynamic systems involving high-dimensional uncertainties. New formulae for Markov Chain convergence assessment are derived. Advanced hybrid Markov Chain Monte Carlo simulation algorithms are also presented in the end.

Next, the problem of how to select the most plausible model class from a set of competing candidate model classes for the system and how to obtain robust predictions from these model classes rigorously, based on data, is ...