A hybrid approach for improving unsupervised fault detection for robotic systems

作者：

Highlights：

• From unsupervised to supervised learning a fault detection model (for robots).

• Insights to why and when it becomes more accurate.

• Theoretical analysis and a prediction tool.

• Empirical results on 3 real-world domains that back these insights.

摘要

•From unsupervised to supervised learning a fault detection model (for robots).•Insights to why and when it becomes more accurate.•Theoretical analysis and a prediction tool.•Empirical results on 3 real-world domains that back these insights.

论文关键词：Fault detection,Robotic systems,Unsupervised

论文评审过程：Received 24 December 2016, Revised 3 March 2017, Accepted 25 March 2017, Available online 29 March 2017, Version of Record 5 April 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.03.058