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- W133113886 abstract "This thesis documents research undertaken on state and parameter estimationtechniques for stochastic systems in a maintenance context. Two individual problemscenarios are considered. For the first scenario, we are concerned with complexsystems and the research involves an investigation into the ability to identify andquantify the occurrence of fault injection during routine preventive maintenanceprocedures. This is achieved using an appropriate delay time modelling specificationand maximum-likelihood parameter estimation techniques. The delay time model ofthe failure process is parameterised using objective information on the failure timesand the number of faults removed from the system during preventive maintenance.We apply the proposed modelling and estimation process to simulated data sets in anattempt to recapture specified parameters and the benefits of improving maintenanceprocesses are demonstrated for the particular example. We then extend the modellingof the system in a predictive manner and combine it with a stochastic filteringapproach to establish an adaptive decision model. The decision model can be used toschedule the subsequent maintenance intervention during the course of anoperational cycle and can potentially provide an improvement on fixed intervalmaintenance policies.The second problem scenario considered is that of an individual component subjectto condition monitoring such as, vibration analysis or oil-based contamination. Theresearch involves an investigation into techniques that utilise condition informationthat we assume is related stochastically to the underlying state of the component,taken here to be the residual life. The techniques that we consider are theproportional hazards model and a probabilistic stochastic filtering approach. Weinvestigate the residual life prediction capabilities of the two techniques and construct relevant replacement decision models. The research is then extended toconsider multiple indicators of condition obtained simultaneously at monitoringpoints. We conclude with a brief investigation into the use of stochastic filteringtechniques in specific scenarios involving limited computational power and variableunderlying relationships between the monitored information and the residual life." @default.
- W133113886 created "2016-06-24" @default.
- W133113886 creator A5023710291 @default.
- W133113886 date "2006-01-01" @default.
- W133113886 modified "2023-09-23" @default.
- W133113886 title "State and parameter estimation techniques for stochastic systems" @default.
- W133113886 hasPublicationYear "2006" @default.
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