Contrastive adversarial domain adaptation for machine remaining useful life prediction

• Main Authors: Mohamed Ragab, Chen, Zhenghua, Wu, Min, Foo, Chuan Sheng, Kwoh, Chee Keong, Yan, Ruqiang, Li, Xiaoli
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Domain Adaptation; Deep Learning
• Online Access: https://hdl.handle.net/10356/157026
• Institution: Nanyang Technological University

Enabling precise forecasting of the remaining useful life (RUL) for machines can reduce maintenance cost, increase availability, and prevent catastrophic consequences. Data-driven RUL prediction methods have already achieved acclaimed performance. However, they usually assume that the training and t...