Special Issue on Cognitive Digital Twins for Predictive Maintenance: Uncertainty and Risk Analysis
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
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These challenges highlight the need for robust uncertainty quantification and risk analysis frameworks tailored to CDTs. This special issue focuses on the intersection of cognitive digital twins, predictive maintenance, and risk analysis, exploring how CDTs can enhance decision-making under uncertainty, improve system reliability, and optimize maintenance strategies. It emphasizes uncertainty quantification, risk modeling, and cognitive capabilities, addressing critical gaps in the field.
Topic Areas
THE SCOPE OF THIS ISSUE INCLUDES BUT IS NOT LIMITED TO:- Cognitive Digital Twins: Fundamentals and Frameworks
- Uncertainty Quantification in Cognitive Digital Twins
- Risk Analysis and Decision-Making in CDTs
- AI-Driven Predictive Maintenance
- Physics-Guided Machine Learning in CDTs
- Stochastic Modeling for Degradation and Failure Prediction
- Industrial Applications of Digital Twins
- Cyber-Physical Security in CDTs
- Explainable AI and Human-Centric CDTs
- Data-driven approaches for predictive maintenance under uncertainty
Submission Instructions
Papers should be submitted electronically to the journal through the ASME Journal Tool. If you already have an account, log in as an author and select Submit Paper. If you do not have an account, you can create one here.Once at the Paper Submittal page, select the ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part B: Mechanical Engineering, and then under the Special Issue field, select Special Issue on Cognitive Digital Twins for Predictive Maintenance: Uncertainty and Risk Analysis.
Papers received after the deadline or papers not selected for the Special Issue may be accepted for publication in a regular issue.
Guest Editors
Ke Feng, Xi’an Jiaotong University, China (kefeng@xjtu.edu.cn)
Minghui Hu, Beijing University of Chemical Technology, China (humh2008@163.com)
Ge Xin, Beijing Jiaotong University, China (ge.xin@bjtu.edu.cn)
Zifei Xu, University of Liverpool, UK (zifei.xu@liverpool.ac.uk)
Yadong Xu, The Hong Kong Polytechnic University, Hong Kong (yadongseu.xu@polyu.edu.hk)