Robust Predictive Maintenance for Robotics via Unsupervised Transfer Learning

Industrial robots play an increasingly important role in a growing number of fields. Since the breakdown of a single robot may have a negative impact on the entire process, predictive maintenance systems have gained importance as an essential component of robotics service offerings. The main shortcoming of such systems is that features extracted from a task typically differ significantly from the learnt model of a different task, incurring false alarms. In this paper, we propose a novel solution based on transfer learning which addresses a well-known challenge in predictive maintenance algorithms by passing the knowledge of the trained model from one task to another in order to prevent the need for retraining and to eliminate such false alarms. The deployment of the proposed algorithm on real-world datasets demonstrates that the algorithm can not only distinguish between tasks and mechanical condition change, it further yields a sharper deviation from the trained model in case of a mechanical condition change and thus detects mechanical issues with higher confidence.