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

Author

Listed:
  • 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)

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.

Suggested Citation

  • K. Jenab & K. Rashidi & S. Moslehpour, 2013. "An Intelligence-Based Model for Condition Monitoring Using Artificial Neural Networks," International Journal of Enterprise Information Systems (IJEIS), IGI Global, vol. 9(4), pages 43-62, October.
    • Handle: RePEc:igg:jeis00:v:9:y:2013:i:4:p:43-62
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