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 system was installed in a live production facility with the goal of improving site maintenance.