Call for Papers
Special Issue on Predictive Reliability of Engineering Systems
Abstract
Many owners, operators, and maintainers of infrastructure and in various industries now prioritise maintenance, reliability, and asset maintenance management. Modern methods for enhancing reliability and maintaining complex engineering systems include predictive maintenance. Proactive maintenance solutions must be used in order to improve system reliability and reduce downtime. However, there are some restrictions for such applications in the actual world that must be studied in depth in order to be overcome the reliability issues of engineering systems.
Implementing proactive maintenance techniques and identifying probable equipment or system problems before they happen is made possible by predictive reliability analysis. This can be accomplished by businesses using a variety of methodologies, such as studying the equipment's performance and maintenance history, predicting future performance and dependability using statistical analysis and predictive modelling methods, and designing strategies. Using data-driven strategies, organisations can decrease downtime, increase productivity, and boost overall operational reliability.
This issue will include an overview of the benefits of data based predictive maintenance models and management frameworksfor balancing costs, risk and performance. The articles submitted to this special issue should address the areas of advanced modelling in following topics:
- Predictive maintenance frameworks
- Intelligent system condition monitoring techniques
- Condition Based Maintenance models
- Machine learning based anomalydetection models
- Signal processing techniques
- Role of fault prediction/prognostics approaches in predictive maintenance
- Data driven modeling-based fault prognostics
- Physics based modeling-based fault prognostics
- Remaining Useful Life (RUL) estimation approaches
- Applications of machine learning for fault prediction
- Applications of prognostics for engineering systems
- Impact of machine learning algorithms in fault prognostics
- Reliability of machine learning based condition monitoring systems
- Maintenance strategy optimization approaches
- Maintenance decisions under uncertainty
- Intelligent fault diagnostics
- Safety critical and high assurance systems
- Artificial Intelligence and autonomous machines
Timeline:
Manuscript Submission Open: November 30, 2023
Manuscript Submission Deadline: May 30, 2024
Guest Editors:
Mangey Ram, Ph.D.
Graphic Era University, Dehradun, India
mangeyram@gmail.com, mangeyram@geu.ac.in
Gunjan Soni, Ph.D.
Malaviya National Institute of Technology Jaipur, India.
gsoni.mech@mnit.ac.in
All submitted papers will be undergone rigorouspeer-reviewing as per the standard of World Scientific Journals.Authors will submit their papers on the IJRQSE online system: https://www.worldscientific.com/loi/ijrqse