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Failure rate prediction with artificial neural networks

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Purpose - To suggest that a multi layer perception based artificial neural network (MLP-ANN) is a practical instrument to evaluate the expected failure rates of 143 centrifugal pumps used in an oil refinery plant. Design/methodology/approach - A MLP is adopted to weigh up the correlation existing among the failure rates and the several different operating conditions which have some influence in the occurrence. Findings - During the training phase, it is possible to discriminate among those variables closely significant for the final outcome and those which can be kept off from the analysis. In particular, the neural network automatically calculates and classifies the centrifugal pumps in terms of both the failure probability and its variability degree, giving a better analysis instrument to take decisions and to justify them, in order to optimise and fully support an eventual preventive maintenance (PM) program. Originality/value - Aids in decision-making to reduce the necessity of reactive maintenance activities and to simplify the planning of PM ones.
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Keywords: Failure (Mechanical); Neural Nets; Preventive Maintenance

Document Type: Research Article

Publication date: 2005-03-01

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