Bayesian Methods in Reliability Analysis with Big Data
Background and Motivation
This special issue brings together cutting-edge research that explores the use of Bayesian methods in reliability analysis with big data. It will provide a platform for sharing novel methodologies, tools, and case studies that demonstrate the effectiveness of Bayesian approaches in addressing real-world reliability problems.
Topics of interest
We invite high-quality submissions that explore, but are not limited to, the following topics:
Bayesian Inference and Modeling:
Bayesian statistical methods for reliability data analysis.
Development of Bayesian models for predicting system failures.
Incorporation of expert knowledge and prior information in Bayesian models.
Big Data Analytics in Reliability Engineering:
Techniques for processing and analyzing large-scale reliability data.
Integration of Bayesian methods with machine learning for big data.
Data fusion techniques for comprehensive reliability assessment.
Predictive Maintenance and Prognostics:
Bayesian approaches for predictive maintenance scheduling.
Prognostic models using Bayesian inference.
Case studies on Bayesian methods in predictive maintenance.
Risk and Uncertainty Management:
Bayesian risk assessment and management strategies.
Handling uncertainty and variability in reliability data with Bayesian methods.
Applications of Bayesian decision theory in reliability engineering.
System Reliability and Safety:
Bayesian reliability models for complex engineering systems.
Safety analysis and risk mitigation using Bayesian approaches.
Case studies on the application of Bayesian methods in system safety.
Computational Methods and Algorithms:
Advanced computational techniques for Bayesian reliability analysis.
Monte Carlo methods and Markov Chain Monte Carlo (MCMC) simulations.
Software tools and frameworks for Bayesian reliability analysis.
Guest editors:
Associate Prof. Piao Chen
Zhejiang University, Hangzhou, China
Email: piaochen@intl.zju.edu.cn;
Dr. Binbin Li
Zhejiang University, Hangzhou, China
Email: binbinli@intl.zju.edu.cn;
Prof. Haitao Liao
University of Arkansas, Fayetteville, AR, USA
Email: liao@uark.edu;
Manuscript submission information:
Authors are invited to submit their papers to the Editorial Manager System of the journal: https://www2.cloud.editorialmanager.com/jress/default2.aspx
When submitting your manuscript please select the article type “VSI: Bayesian methods”. Please submit your manuscript before the submission deadline.
The submission deadline is 30-Nov-2025;
Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and the link to submit your manuscript is available on the Journal’s homepage: https://www.sciencedirect.com/journal/reliability-engineering-and-system-safety
Reliability Engineering & System Safety的CAR指数
2023年3月份科睿唯安官方一次性踢除35本SCI期刊，多数涉及学术诚信问题，让我们意识到学术期刊的“被踢”指数，也很重要。目前，对于期刊的“被踢”指数，这里介绍一下：CAR指数（关于CAR的详细介绍，请关注：www.jcarindex.com），这是一种评价期刊学术诚信风险的指数，指数越高代表可能的风险越大。从数据看，Reliability Engineering & System Safety不管是2023年度，还是2024年度实时的CAR指数，都是比较低的。当然，CAR指数仅供参考，期刊风险情况，需以科睿唯安或中科院预警等官方为准！
让推送更美好~
