Event Details
Granada, Spain, May 28-29, 2026
Reliability, maintenance and safety in the data-driven era
In a context of increasing system complexity, digital transformation, and growing societal demands for safety, resilience, vulnerability reduction, and sustainability, the field of reliability and maintenance engineering is undergoing a profound evolution. This year’s Spring Meeting aims to serve as a cross-disciplinary platform for researchers, practitioners, and industry experts to exchange cutting-edge developments and strategic insights. At its core, the meeting focuses on how data, models, and algorithms can transform the design, monitoring, and maintenance of engineering systems, both in theory and in practice. We welcome both contributions that advance traditional maintenance and reliability models and those that push boundaries using AI-based methods to support smarter and more adaptive systems. Beyond technical depth, the conference will encourage discussions on strategic challenges and opportunities in the field, including the deployment of digital twins, integration of AI into safety-critical systems, and the incorporation of sustainability into reliability-centered maintenance planning. The goal of this Spring Meeting is to facilitate a dynamic exchange of ideas that promotes scientific advancement and its effective translation into practical solutions, fostering meaningful collaboration with industry and society in the reliability and maintenance field.Topics of interest include, but are not limited to:
- Data-driven reliability and maintenance.
- Stochastic processes for reliability and failure modelling.
- Condition monitoring, diagnostics, and prognostics.
- Predictive maintenance and digital twins.
- Machine learning and neural networks in reliability engineering.
- Structural health monitoring and sensor system design.
- Uncertainty and sensitivity analysis for safety evaluation of critical structures.
- Value of information analysis for improved decision-making in structural safety.
- Resilience engineering and infrastructure systems management.
- Sustainable maintenance.
- Bayesian methods in reliability and maintenance modelling.
- Partially Observable Markov Processes for Condition-Based Maintenance.
- Deep Learning for Remaining Useful Life (RUL) Prediction.
- Hybrid AI-Statistical Models for Fault Diagnosis.
- Reliability and Maintenance Strategies for Industry 5.0.
