Generic Bayesian network models for making maintenance decisions from available data and expert knowledge

To maximise asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. Various statistical models have been proposed for predicting this, but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how (1) data on the condition of assets available from their periodic inspection can be used, (2) failure data from related groups of asset can be combined using judgement from experts and (3) expert knowledge of the deterioration’s causes can be combined with statistical data to adjust predictions. A case study of bridges on the rail network in Great Britain (GB) is presented, showing how the model could be used for the maintenance decision problem, given typical data likely to be available in practice

On the thermodynamic boundary conditions of a solidifying mushy layer with outflow

AbstractThe free-boundary problem between a liquid region and a mushy layer (a reactive porous medium) must respect both thermodynamic and fluid dynamical considerations. We develop a steady two-dimensional forced-flow configuration to investigate the thermodynamic condition of marginal equilibrium that applies to a solidifying mushy layer with outflow and requires that streamlines are tangent to isotherms at the interface. We show that a ‘two-domain’ approach in which the mushy layer and liquid region are distinct domains is consistent with marginal equilibrium by extending the Stokes equations in a narrow transition region within the mushy layer. We show that the tangential fluid velocity changes rapidly in the transition region to satisfy marginal equilibrium. In convecting mushy layers with liquid channels, a buoyancy gradient can drive this tangential flow. We use asymptotic analysis in the limit of small Darcy number to derive a regime diagram for the existence of steady solutions. Thus we show that marginal equilibrium is a robust boundary condition and can be used without precise knowledge of the fluid flow near the interface.This research began as a project between D. Conroy and M.G.W. at the Geophysical Fluid Dynamics Program: Woods Hole Oceanographic Institution (2006). We gratefully acknowledge helpful discussions with T. Schulze.This is the accepted manuscript for a paper Journal of Fluid Mechanics, Volume 762, January 2015, R1 (12 pages) © 2014 Cambridge University Press, DOI: 10.1017/jfm.2014.65

Bayesian Network Models for Making Maintenance Decisions from Data and Expert Judgment

To maximize asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. A number of types of statistical model have been proposed for predicting this but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how i) failure data from related groups of asset can be combined, ii) data on the condition of assets available from their periodic inspection can be used iii) expert knowledge of the causes deterioration can be combined with statistical data to adjust predictions and iv) the uncertain effects of maintenance actions can be modelled. We show how the model could be used for a range of decision problems, given typical data likely to be available in practice

Compatible and incompatible abstractions in Bayesian networks

The graphical structure of a Bayesian network (BN) makes it a technology well-suited for developing decision support models from a combination of domain knowledge and data. The domain knowledge of experts is used to determine the graphical structure of the BN, corresponding to the relationships and between variables, and data is used for learning the strength of these relationships. However, the available data seldom match the variables in the structure that is elicited from experts, whose models may be quite detailed; consequently, the structure needs to be abstracted to match the data. Up to now, this abstraction has been informal, loosening the link between the final model and the experts' knowledge. In this paper, we propose a method for abstracting the BN structure by using four 'abstraction' operations: node removal, node merging, state-space collapsing and edge removal. Some of these steps introduce approximations, which can be identified from changes in the set of conditional independence (CI) assertions of a network

Towards A Model-Based Asset Deterioration Framework Represented by Probabilistic Relational Models

Most asset deterioration tools are designed for a specific application, as a consequence, a small change of the specification may result in a complete change of the tool. Inspired by the model-based approach of separating problem specification from analysis technique, we propose a model-based asset deterioration assessment framework using probabilistic relational models. The probabilistic relational models express abstract probabilistic dependency covers a range of deterioration modelling assumptions. An expert in the domain of asset deterioration can then use his knowledge of the factors that affect deterioration to customise the abstract models to a specific application, without requiring a detailed understanding the underlying computational framework. We illustrate the use of the framework with multiple variants of deterioration models

Why Risk Models should be Parameterised

Risk models using fault and event trees can be extended with explicit factors, which are states of the system, its users or its environment that influence event probabilities. The factors act as parameters in the risk model, enabling the model to be re-used and also providing a new way to estimate the overall risk of a system with many instances of the risk. A risk model with parameters can also be clearer

A Progressive Explanation of Inference in ‘Hybrid’ Bayesian Networks for Supporting Clinical Decision Making

Many Bayesian networks (BNs) have been developed as decision support tools. However, far fewer have been used in practice. Sometimes it is assumed that an accurate prediction is enough for useful decision support but this neglects the importance of trust: a user who does not trust a tool will not accept its advice. Giving users an explanation of the way a BN reasons may make its predictions easier to trust. In this study, we propose a progressive explanation of inference that can be applied to any hybrid BN. The key questions that we answer are: which important evidence supports or contradicts the prediction and through which intermediate variables does the evidence flow. The explanation is illustrated using different scenarios in a BN designed for medical decision support