Abstract:
Condition monitoring together with predictive maintenance of electric motors and other equipment used by the industry avoids severe economic losses resulting from unexpec...Show MoreMetadata
Abstract:
Condition monitoring together with predictive maintenance of electric motors and other equipment used by the industry avoids severe economic losses resulting from unexpected motor failures and greatly improves the system reliability. This paper describes a Machine Learning architecture for Predictive Maintenance, based on Random Forest approach. The system was tested on a real industry example, by developing the data collection and data system analysis, applying the Machine Learning approach and comparing it to the simulation tool analysis. Data has been collected by various sensors, machine PLCs and communication protocols and made available to Data Analysis Tool on the Azure Cloud architecture. Preliminary results show a proper behavior of the approach on predicting different machine states with high accuracy.
Published in: 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)
Date of Conference: 02-04 July 2018
Date Added to IEEE Xplore: 30 August 2018
ISBN Information: