Fault Classification in Predictive Maintenance

Abstract

In the field of predictive maintenance, “smart condition monitoring” extends beyond merely assessing the present state of manufacturing equipment. In today's landscape, businesses and research endeavors strive for deeper insights into equipment conditions, seeking to uncover potential causes behind issues or anticipate the remaining lifespan. This paper examines an approach focused on classifying anomalous behavior of bearings and map them to historically similar fault patterns. This study aims to contribute valuable insights to the field of condition monitoring and predictive maintenance practices.