Special Issue on Uncertainty-Aware Diagnostics and Prognostics for Health Monitoring and Risk Management of Engineered Systems
In recent decades, fault diagnostics and failure prognostics have demonstrated their great potential for health monitoring and risk management of complex engineering systems, including smart factories, power plants, space systems, and heavy equipment.
The credibility and applicability of fault diagnostics and failure prognostics, however, are significantly affected by various uncertainties, such as model uncertainty, data uncertainty, process uncertainty, environmental uncertainty, and the inherent uncertainty of engineered systems. Therefore, accurately quantifying the effects of these uncertainties is essential and one of the most widely-held concerns to ensure trustworthy decision-making based on diagnostic and prognostic results.
The purpose of this special issue is to present the latest advancements in the field of uncertainty-aware diagnostics and prognostics for the health management of engineered systems.
Topic AreasTHE SCOPE OF THIS ISSUE INCLUDES BUT IS NOT LIMITED TO:
- Uncertainty-aware machine learning models for diagnostics and prognostics
- Uncertainty quantification and reduction of predictive model
- Physics-informed and data-driven probabilistic degradation modeling
- Trustworthy fault diagnostics under uncertainty
- Confidence-based / robust remaining useful life estimation
- Probabilistic physics-informed machine learning for diagnostics and prognostics
- Uncertainty management for diagnostics and prognostics
- Decision-making based on uncertainty-aware prognostics
- Probabilistic and non-probabilistic approaches in diagnostics and prognostics
- Highly efficient uncertainty propagation techniques in complex engineering systems
- Applications of uncertainty-aware diagnostics and prognostics
Submission InstructionsPapers 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 Uncertainty-Aware Diagnostics and Prognostics for Health Monitoring and Risk Management of Engineered Systems
Papers received after the deadline or papers not selected for the Special Issue may be accepted for publication in a regular issue.
Chen Jiang, Assistant Professor, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, China, (email@example.com)
Zhen Hu, Associate Professor, Department of Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn, USA, (firstname.lastname@example.org)
Alba Sofi, Associate Professor, Department of Architecture and Territory, University Mediterranea of Reggio Calabria, Italy, (email@example.com)
Liang Gao, Professor, Department of Industrial and Manufacturing System Engineering, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, China, (firstname.lastname@example.org)
Haobo Qiu, Professor, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, China, (email@example.com)
Byeng D. Youn, Professor, Department of Mechanical Engineering, Seoul National University, Korea (firstname.lastname@example.org)