Title: Intelligence-based condition monitoring model
Authors: Kamyar Rashidi; Kouroush Jenab
Department of Mechanical and Industrial Engineering, Ryerson University, 350 Victoria Street, Toronto, Canada
Society of Reliability Engineering-Ottawa, 761 Bay Street, Unit 812, Toronto, Ontario, Canada
Abstract: Condition-based maintenance (CBM) is a maintenance strategy that reduces equipment downtime, production loss, and maintenance cost based on changes in equipment condition (e.g., changes in vibration, changes in power usage, changes in operating performance, changes in temperatures, changes in noise levels, changes in chemical composition, increase in debris content and changes in volume of material). In this study, we present the newly developed condition monitoring model (CMM) based on Bayesian decision theory, which takes vibration signals from the equipment, and classifies them to either normal or abnormal condition. Using conditional risk function, the equipment condition can be classified to either normal or abnormal condition. The conditional risk function is calculated based on loss table and the class posterior probabilities. The developed model can efficiently avoid unnecessary maintenance and make timely actions through analysing the received vibration signals from the equipment. An illustrative example is demonstrated to present the application of the model. Also, the results derived from CMM programme coded in Visual Basic are discussed.
Keywords: maintenance management; condition-based maintenance; CBM; maintenance loss function; Bayesian decision theory; artificial intelligence; condition monitoring; intelligent decision making; vibration signals.
Int. J. of Industrial and Systems Engineering, 2013 Vol.13, No.2, pp.250 - 261
Available online: 31 Jan 2013