Digital twin uses digital technologies to describe and model the characteristics, behaviors, and processes of a device or a system in real world, which has aroused wide concern in aerospace engineering, smart factory and smart building. This paper inspected the......
As a highly secure and reliable system, aviation faces challenges including digital transformation, high production operation costs, low maintenance efficiency, and intensive technology. One of the most beneficial technologies is the digital twin (DT), which can solve the above issues. This paper inve...
The review also provides a comprehensive survey of DT applications from two perspectives: (1) applications in four product-lifecycle phases, i.e. product design, manufacturing, operation and maintenance, and recycling and (2) applications in four categorized engineering fields, including aerospace ...
"Digital Twin Technology Challenges and Applications: A Comprehensive Review" 10.3390/rs14061335 Google Scholar 12 C.K. Lo, C.H. Chen, R.Y. Zhong "A review of the digital twin in product design and development" 10.1016/j.aei.2021.101297 ...
Till now, the exploration of PHM based on digital twin technology has never stopped and has become more and more in-depth. However, there are some challenges in introducing the concept of Digital Twin in the PHM field [5,6]. The core difficulty is the modeling of complex systems [7], ...
Ensuring operational integrity in large-scale equipment hinges on effective fault prediction and health management. Prognostics and health management (PHM) face the challenge of accurately predicting remaining useful life (RUL) using multivariate sensor
The deep residual shrinkage network is a variant of deep residual networks. - zhao62/Deep-Residual-Shrinkage-Networks
Age-related physiological changes in humans are linearly associated with age. Naturally, linear combinations of physiological measures trained to estimate chronological age have recently emerged as a practical way to quantify aging in the form of biologi
Although the applications of artificial intelligence especially deep learning have greatly improved various aspects of intelligent manufacturing, they stil
including tracking-error constrained feed rate scheduling, inverse dynamics for vibration avoidance, and advanced contour error estimation algorithms have been done (Kim & Okwudire,2023; Yang et al.,2020; Du et al.,2020). The application of digital twin technology has been explored to incorporate...