Call for papers
Prospective authors are invited to submit a maximum of 2-pages abstract. Both research and industry contributions are welcomed. Experts will review submitted abstracts based on significance, innovation, technical validity, clarity, and readability. The authors of accepted abstracts will be invited to present their work at the conference.
The abstracts can be submitted using the template and the submission system that will soon be available here.
Authors with accepted abstracts are also invited to submit a full paper version of their work. The papers will also be reviewed by a panel of experts and published in the conference proceedings. The proceedings will be open access and stored in a public repository.
Special issues in journals of the area are being considered to publish high-quality papers of works presented at the conference.
For more information, please contact the Technical Committee Chairs at cbmacademy-LR@tudelft.nl.
Important dates
June 2021 − Call for papers
November 26th, 2021 − Abstract Due
December 28th, 2021 − Abstract Acceptance
March 26th, 2022 − Papers Due (optional)
May 7th, 2022 − Papers Acceptance
May 24th-25th, 2022 − Conference
TOPICS OF INTEREST
Topics of interest for submission include, but are not limited to:
-
Prognostics and Health Management
• Predictive maintenance algorithms
• Health monitoring techniques
• Machine learning/data-driven algorithms
• Physics-based and hybrid models
• Explainable and traceable AI -
Structural Health Monitoring
• Diagnostic solutions
• Prognostic solutions
• Digital-twins & model-based solutions
• Sensing technologies -
Reliability and safety
• Reliability for predictive maintenance
• Standards & certification
• Safety assessment
• Uncertainty analysis
-
IT enablers
• Intelligent maintenance systems
• Digital solutions & e-maintenance
• Federated analytics
• Data collection and analysis
• Data transmission, communication & storage
• Data sharing solutions
• Security & data integrity
• Big data solutions for CBM
• Edge computing solutions -
Maintenance planning and practice
• Scheduling solutions
• Maintenance support modelling & simulation
• Inventory management
• Economic & operations studies
• Maintenance policies and strategies
-
Prognostics and Health Management
• Predictive maintenance algorithms
• Health monitoring techniques
• Machine learning/data-driven algorithms
• Physics-based and hybrid models
• Explainable and traceable AI -
Structural Health Monitoring
• Diagnostic solutions
• Prognostic solutions
• Digital-twins & model-based solutions
• Sensing technologies -
Reliability and safety
• Reliability for predictive maintenance
• Standards & certification
• Safety assessment
• Uncertainty analysis
-
IT enablers
• Intelligent maintenance systems
• Digital solutions & e-maintenance
• Federated analytics
• Data collection and analysis
• Data transmission, communication & storage
• Data sharing solutions
• Security & data integrity
• Big data solutions for CBM
• Edge computing solutions -
Maintenance planning and practice
• Scheduling solutions
• Maintenance support modelling & simulation
• Inventory management
• Economic & operations studies
• Maintenance policies and strategies