Earticle

초록

영어

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.

목차

Abstract
1. 서론
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

키워드

저자

• 김재은 [ Jae-Eun Kim | 울산대학교 대학원 ]
• 장길상 [ Gil-Sang Jang | 울산대학교 경영정보학과 ] Corresponding Author
• 임국화 [ KuK-Hwa Lim | 울산대학교 경영정보학과 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  대한안전경영과학회지 [Journal of Korea Safety Management & Science]

• 간기

  계간

• pISSN

  1229-6783

• eISSN

  2288-1484

• 수록기간

  1999~2025

• 십진분류

  KDC 530 DDC 620