Asset inspection and maintenance is an important activity. The ability to use data from inspections wisely, and to select how and when to inspect next, offers the opportunity for improved decision making: better use of inspection and maintenance resources, and reduced risk of asset failure. At present, statistical tools for inspection analysis and planning are usually based on separate and independent analyses of individual components.
Compared to existing methods for inspection analysis and planning, the model introduced here is novel in that:
- It allows direct analysis of large multivariate systems, rather than modelling components separately and independently
- It permits use of data from partial inspections at arbitrary times, yet we learn about the whole system
- We learn about the uncertainties in wall thickness and corrosion rate per component, and the dependencies between these across the system, rather than assuming these uncertainties and dependencies known and fixed beforehand
- It allows economically-optimal future inspection strategies to be estimated consistently.
In due course, the intention is to implement the method, with accompanying algorithms for optimal inspection design, within Shell's S-IDAP software.
The proposed work plan would involve the development of prototype Bayesian Inspection Planning software.
Software would be in the form of a MATLAB toolbox (for Windows / Linux), with major challenges including development of a useable user interface, particularly for problem specification this might be a set of input files expected by the software initially, evolving to GUIs. Issues of elicitation of expert beliefs will also provide a challenge to be incorporated into any such software.
Efficient implementation of existing prototype algorithms would be needed for methods to work as production code. The generic nature of methodology would make it applicable widely. However there will be an initial application to inspection planning allowing software to be used as a decision support tool.
Project staff and support
David Randell (Intern, University of Durham)
Philip Jonathan (Company Supervisor, Shell Projects and Technology)
Michael Goldstein (Academic Mentor, University of Durham)
Lorcan Mac Manus (Technology Translator, Industrial Mathematics KTN)
This Internship project is being carried out at the Shell Projects and Technology, in conjunction with the University of Durham. It is part of the KTN's Industrial Mathematics Internships Programme, co-funded by EPSRC. Start date: April 2010; duration: 6 months.
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