Decision Support

Over a number of years we have carried out studies for the Royal Navy to establish the optimal upkeep cycle for a range of classes of warship, based on RCM analyses for each class. This includes the processes for estimating asset management loadings for new build, where principal variables include scheme of complement, waterfront/OEM support, assets to support warship functions and defensible maintenance policies, all of which have a fundamental effect on through life costings. For in-service assets, this process has been widened to include a numerical representation of asset material state in the form of a percentage figure of merit which describes the asset's fitness for purpose to achieve role requirements within the short to medium term. Further details can be found here.

Work undertaken for the British Army has focussed on the peridicities for checking to ensure structural integrity of armoured fighting vehicles in the face of ballistic attack. For a number of RCM reasons this check is reactive - and the frequency of the check is based on a number of implicit risk issues. A tool for calculating a defensible frequency for each structurally-significant item (SSI) has been developed in Visual Basic using the Simple Multi-attribute Rating Technique (SMART); this tool displays the SSI inspection intervals for any chosen vehicle zone (e.g. engine compartment, gearbox compartment, driver's compartment, etc) in ascending inspection interval order and this allows a suitable inspection interval for the zone as a whole to be established. The tool can also be used in reverse by evaluating the risk/cost tradeoff in adjusting the inspection intervals.

In a series of studies we have demonstrated that GA techniques could yield useful results in optimal spares allocation. These studies showed what spares should be carried on board and at locations further back in the logistics chain to support an RCM-derived maintenance strategy. The modelling approach also estimates the degree of uncertainty associated with the final result, given the qualitative nature of input data such as reliability estimates, costs and consequence of stockouts.

In the classic 'bearings only' target motion analysis scenario we have also shown that GA techniques provide solution convergence that is comparable with non-linear least squares and Kalman filtering methods, whilst avoiding the need for complex matrix mathematics.

We have used Kalman filtering techniques to 'observe' otherwise unmeasurable plant data - for example, to estimate seawater exit temperature within a seawater-cooled heat exchanger system, where only seawater ambient temperature and freshwater inlet/outlet temperature measurements are known. In this arrangement, the heat transfer efficiency - and hence tube stack condition - can be estimated and this provides a reliable trigger for tube cleaning maintenance.

Our decision support experience also includes a number of studies using artificial neural network approaches to system modelling within a fault diagnostic arrangement; these approaches have shown a high degree of solution robustness in the presence of noise, together with short model development times.


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Site last updated June 2014