  Industrial robots play an increasingly important role in a growing number of
fields. For example, robotics is used to increase productivity while reducing
costs in various aspects of manufacturing. Since robots are often set up in
production lines, the breakdown of a single robot has a negative impact on the
entire process, in the worst case bringing the whole line to a halt until the
issue is resolved, leading to substantial financial losses due to the
unforeseen downtime. Therefore, predictive maintenance systems based on the
internal signals of robots have gained attention as an essential component of
robotics service offerings. The main shortcoming of existing predictive
maintenance algorithms is that the extracted features typically differ
significantly from the learnt model when the operation of the robot changes,
incurring false alarms. In order to mitigate this problem, predictive
maintenance algorithms require the model to be retrained with normal data of
the new operation. In this paper, we propose a novel solution based on transfer
learning to pass the knowledge of the trained model from one operation to
another in order to prevent the need for retraining and to eliminate such false
alarms. The deployment of the proposed unsupervised transfer learning algorithm
on real-world datasets demonstrates that the algorithm can not only distinguish
between operation 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.
