Study on Fault Diagnosis and Data Processing Techniques for Substrate Transfer Robots Using Vibration Sensor Data

• 영문명: Study on Fault Diagnosis and Data Processing Techniques for Substrate Transfer Robots Using Vibration Sensor Data
• 발행기관: 한국마이크로전자및패키징학회
• 저자명: MD Saiful Islam Mi-Jin Kim Kyo-Mun Ku Hyo-Young Kim Kihyun Kim
• 간행물 정보: 『마이크로전자 및 패키징학회지』제31권 제2호, 45~53쪽, 전체 9쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2024.06.30

국문 초록

The maintenance of semiconductor equipment is crucial for the continuous growth of the semiconductor market. System management is imperative given the anticipated increase in the capacity and complexity of industrial equipment. Ensuring optimal operation of manufacturing processes is essential to maintaining a steady supply of numerous parts. Particularly, monitoring the status of substrate transfer robots, which play a central role in these processes, is crucial. Diagnosing failures of their major components is vital for preventive maintenance. Fault diagnosis methods can be broadly categorized into physics-based and data-driven approaches. This study focuses on data-driven fault diagnosis methods due to the limitations of physics-based approaches. We propose a methodology for data acquisition and preprocessing for robot fault diagnosis. Data is gathered from vibration sensors, and the data preprocessing method is applied to the vibration signals. Subsequently, the dataset is trained using Gradient Tree-based XGBoost machine learning classification algorithms. The effectiveness of the proposed model is validated through performance evaluation metrics, including accuracy, F1 score, and confusion matrix. The XGBoost classifiers achieve an accuracy of approximately 92.76% and an equivalent F1 score. ROC curves indicate exceptional performance in class discrimination, with 100% discrimination for the normal class and 98% discrimination for abnormal classes.

목차

1. Introduction

2. Robot Component Fault Diagnosis Testbed

3. Experimental Methods and Data Acquisition System

4. Raw Data Processing and Visualization

5. Algorithms for Fault Diagnosis

6. Conclusion

Acknowledgments

References

키워드

Transfer robot Fault diagnosis Vibration sensor Data preprocessing Machine learning