Performance Evaluation Technique of Learning Model Based on Feature Cluster in Sensing Data of Collaborative Robots

Jinse Kim, Subin Bea, Ye-Seul Park, Jung-Won Lee

http://doi.org/10.5626/JOK.2022.49.10.824

Recently, attempts have been made to apply an artificial intelligence model to PHM(Prognostics and Health Management) of collaborative robots, a representative equipment of smart factories. However, typical models are developed in a heuristic way without preprocessing or analysis of sensing data collected by operating test programs. Therefore, in this paper, we proposed a model performance evaluation method based on feature cluster concept which could analyze features of time series sensing data with cycles collected from cooperative robots. To demonstrate the effectiveness of the proposed method, we applied it to a program classification model, an internal component of the motion fault detection network, and identified characteristics of data that contributed to performance degradation, which has not been revealed by existing method. This results enabled a qualitative evaluation of the performance of the model and provided directions to improving model performance.