Strengthening Digital Twin Applications based on Machine Learning for Complex Equipment
Zijie Rena and Jiafu Wanb
School of Mechanical and Automotive Engineering South China University of Technology Guangzhou, China
ame zijieren@mail.scut.edu.cn
bmejwan@scut.edu.cn
ABSTRACT
Digital twin technology and machine learning are emerging technologies in recent years. Through digital twin technology, it is virtually possible to virtualize a product, process or service and the information interaction and co-evolution between physical and information world. Machine Learning (ML) can improve the cognitive, reasoning and decision-making abilities of the digital twin through knowledge extraction. The full life cycle management of complex equipment is considered the key to the intelligent transformation and upgrading of the modern manufacturing industry. The application of the above two technologies in the full life cycle management of complex equipment is going to make each stage of the life cycle more responsive, predictable and adaptable. In this study, we have proposed a full life cycle digital twin architecture for complex equipment. We have described four specific scenarios in which two typical machine learning algorithms based on deep reinforcement learning are applied which are further used to enhance digital twin in various stages of complex equipment. At the end of this study, we have summarized the application advantages of the combination of digital twin and machine learning while addressing future research direction in this domain.
Keywords: Complex Equipment, Digital Twin, Deep Reinforcement Learning, Full Life Cycle Management.
