In this paper, we propose a method for diagnosing overload and working load of collaborative robots through performance analysis of machine learning algorithms. To this end, an experiment was conducted to perform pick & place operation while changing the payload weight of a cooperative robot with a payload capacity of 10 kg. In this experiment, motor torque, position, and speed data generated from the robot controller were collected, and as a result of t-test and f-test, different characteristics were found for each weight based on a payload of 10 kg. In addition, to predict overload and working load from the collected data, machine learning algorithms such as Neural Network, Decision Tree, Random Forest, and Gradient Boosting models were used for experiments. As a result of the experiment, the neural network with more than 99.6% of explanatory power showed the best performance in prediction and classification. The practical contribution of the proposed study is that it suggests a method to collect data required for analysis from the robot without attaching additional sensors to the collaborative robot and the usefulness of a machine learning algorithm for diagnosing robot overload and working load.

Abstract
1. 서 론
2. 이론적 배경
3. 머신러닝 알고리즘의 고찰
    3.1 Neural Network
    3.2 Decision Tree
    3.3 Random Forest
    3.4 Gradient Boosting
    3.5 적용된 Algorithm parameters
4. 통계분석을 통한 데이터 특성 분석
    4.1 Data collection
    4.2 Histogram 분석
    4.3 t-test와 f-test 분석
5. 머신러닝 알고리즘의 성능비교 분석
    5.1 가반하중 예측 모델링
    5.2 가반하중 분류 모델링
    5.3 영향 변수 중요도
6. 결론 및 향후연구
7. References

• 김재은(울산대학교 대학원) | Jae-Eun Kim (Graduate School of Business Administration, University of Ulsan)
• 장길상(울산대학교 경영정보학과) | Gil-Sang Jang (Deptment of Management Information Systems, University of Ulsan) Corresponding Author
• 임국화(울산대학교 대학원) | KuK-Hwa Lim (Graduate School of Business Administration, University of Ulsan)