Publisher： IGI Global_journal
E-ISSN： 1548-1123|9|4|43-62
ISSN： 1548-1115
Source： International Journal of Enterprise Information Systems (IJEIS), Vol.9, Iss.4, 2013-10, pp. : 43-62
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Abstract
This paper reports a newly developed Condition-Based Maintenance (CBM) model based on Artificial Neural Networks (ANNs) which takes into account a feature (e.g., vibration signals) from a machine to classify the condition into normal or abnormal. The model can reduce equipment downtime, production loss, and maintenance cost based on a change in equipment condition (e.g., changes in vibration, power usage, operating performance, temperatures, noise levels, chemical composition, debris content, and volume of material). The model can effectively determine the maintenance/service time that leads to a low maintenance cost in comparison to other types of maintenance strategy. Neural Networks tool (NNTool) in Matlab is used to apply the model and an illustrative example is discussed.
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