Computing Model of Condition Monitoring and Failure Prevention in Industry Assets Márcio Correa, Dorotéa Vilanova Garcia

Abstract

 In any industry, asset condition monitoring is vital and has been enhanced with new technologies and methodologies aiming failure predictions and optimization of time and costs related to corrective and preventive maintenance. Several factors can be taken into consideration when determining the condition of an asset, from electrical parameters such as power and current consumption to mechanical parameters such as vibration and thermals such as ambient and asset temperature. With plenty of data, a computational model is needed that can determine what and when a given asset will, or may fail.