State and parameter estimation techniques for stochastic systems
Carr, Matthew J. 2006, State and parameter estimation techniques for stochastic systems , PhD thesis, Salford : University of Salford.
Restricted to Repository staff only until 03 October 2014.
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This thesis documents research undertaken on state and parameter estimation techniques for stochastic systems in a maintenance context. Two individual problem scenarios are considered. For the first scenario, we are concerned with complex systems and the research involves an investigation into the ability to identify and quantify the occurrence of fault injection during routine preventive maintenance procedures. This is achieved using an appropriate delay time modelling specification and maximum-likelihood parameter estimation techniques. The delay time model of the failure process is parameterised using objective information on the failure times and the number of faults removed from the system during preventive maintenance. We apply the proposed modelling and estimation process to simulated data sets in an attempt to recapture specified parameters and the benefits of improving maintenance processes are demonstrated for the particular example. We then extend the modelling of the system in a predictive manner and combine it with a stochastic filtering approach to establish an adaptive decision model. The decision model can be used to schedule the subsequent maintenance intervention during the course of an operational cycle and can potentially provide an improvement on fixed interval maintenance policies. The second problem scenario considered is that of an individual component subject to condition monitoring such as, vibration analysis or oil-based contamination. The research involves an investigation into techniques that utilise condition information that 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 the proportional hazards model and a probabilistic stochastic filtering approach. We investigate the residual life prediction capabilities of the two techniques and construct relevant replacement decision models. The research is then extended to consider multiple indicators of condition obtained simultaneously at monitoring points. We conclude with a brief investigation into the use of stochastic filtering techniques in specific scenarios involving limited computational power and variable underlying relationships between the monitored information and the residual life.
|Item Type:||Thesis (PhD)|
|Schools:||Colleges and Schools > College of Business & Law > Salford Business School|
|Depositing User:||Institutional Repository|
|Date Deposited:||03 Oct 2012 14:34|
|Last Modified:||07 Apr 2013 12:36|
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