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

Authors:
K. Jenab;K. Rashidi;S. Moslehpour
Affiliations:
Society of Reliability Engineering, Ottawa, Canada;Department of Mechanical Engineering, Ryerson University, Toronto, Canada;Department of Electrical Engineering, Hartford University, West Hartford, CT, USA
Venue:
International Journal of Enterprise Information Systems
Year:
2013

Citing 2
Cited 0

Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
An overview of time-based and condition-based maintenance in industrial application

Computers and Industrial Engineering

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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.