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

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

K. Jenab, K. Rashidi, S. Moslehpour
Copyright: © 2013 |Volume: 9 |Issue: 4 |Pages: 20
ISSN: 1548-1115|EISSN: 1548-1123|EISBN13: 9781466635500|DOI: 10.4018/ijeis.2013100104
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MLA

Jenab, K., et al. "An Intelligence-Based Model for Condition Monitoring Using Artificial Neural Networks." IJEIS vol.9, no.4 2013: pp.43-62. http://doi.org/10.4018/ijeis.2013100104

APA

Jenab, K., Rashidi, K., & Moslehpour, S. (2013). An Intelligence-Based Model for Condition Monitoring Using Artificial Neural Networks. International Journal of Enterprise Information Systems (IJEIS), 9(4), 43-62. http://doi.org/10.4018/ijeis.2013100104

Chicago

Jenab, K., K. Rashidi, and S. Moslehpour. "An Intelligence-Based Model for Condition Monitoring Using Artificial Neural Networks," International Journal of Enterprise Information Systems (IJEIS) 9, no.4: 43-62. http://doi.org/10.4018/ijeis.2013100104

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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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