An Intelligence-Based Model for Condition Monitoring Using Artificial Neural Networks

K. Jenab, K. Rashidi and S. Moslehpour
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K. Jenab: Society of Reliability Engineering, Ottawa, Canada
K. Rashidi: Department of Mechanical Engineering, Ryerson University, Toronto, Canada
S. Moslehpour: Department of Electrical Engineering, Hartford University, West Hartford, CT, USA

International Journal of Enterprise Information Systems (IJEIS), 2013, vol. 9, issue 4, 43-62

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.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jeis00:v:9:y:2013:i:4:p:43-62

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