A Data-driven Method for Monitoring Systems that Operate Repetitively – Applications to Robust Wear Monitoring in an Industrial Robot Joint

This paper presents a method for condition monitoring of systems that operate in a repetitive manner. A data driven method is proposed that considers changes in the distribution of data samples obtained from multiple executions of one or several tasks. This is made possible with the use of kernel density estimators and the Kullback-Leibler distance measure between distributions. To increase robustness to unknown disturbances and sensitivity to faults, the use of a weighting function is suggested which can considerably improve detection performance. The method is very simple to implement, it does not require knowledge about the monitored system and can be used without process interruption, in a batch manner. The method is illustrated with applications to robust wear monitoring in a robot joint. Interesting properties of the application are presented through a real case study and simulations. The achieved results show that robust wear monitoring in industrial robot joints is made possible with the proposed method.