State and parameter estimation techniques for stochastic systems

Carr, MJ

Abstract

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.

Citation

Carr, M. State and parameter estimation techniques for stochastic systems. (Thesis). Salford : University of Salford

• Thesis Type: Thesis
• Deposit Date: Oct 3, 2012
• Publicly Available Date: Oct 3, 2012
• Award Date: Jan 1, 2006