Vibration Anomaly Detection using Deep Autoencoders for Smart Factory

Abstract

Early fault detection in production is crucial for manufacturing facilities to prevent unplanned downtimes and maximise the operational life of equipment. The aim of this paper is to present a method of anomaly detection for an in service motor using self-supervised learning. The authors have developed a condition monitoring system for a Smart Factory using deep autoencoders. The systems was installed in a live production facility with the goal of improving site maintenance.