Topic: Digital twins for O&M based on vibrations: A PLM approach

Dr. Diego Galar is a Full Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering at LTU, Luleå University of Technology. He has led coordination efforts for numerous European projects, covering a wide range of topics such as cyber-physical systems, Industry 4.0, IoT, and Industrial AI and Big Data. Dr. Galar has also been actively engaged in projects with the Swedish industry and those funded by Swedish national agencies like Vinnova.

In addition to his academic role, he serves as the Chief Technology Officer at TSI (Spain), a company specializing in predictive maintenance solutions and condition monitoring for critical defense, marine, and energy assets. He previously worked as the principal researcher at Tecnalia (Spain), where he led the Maintenance and Reliability research group within the Division of Industry and Transport. He also held the Volvo chair as a Professor at Skovde University.

Dr. Galar has an extensive publication record, including over five hundred journal and conference papers, books, and technical reports in the field of maintenance. He actively contributes to the academic community by serving on editorial boards, participating in scientific committees, and chairing international journals and conferences. He is also involved in national and international committees for standardization and R&D in the areas of reliability and maintenance.

Internationally, Dr. Galar has held visiting professorships at institutions such as the Polytechnic of Braganza (Portugal), University of Valencia, NIU (USA), and the Universidad Pontificia Católica de Chile. Currently, he continues to serve as a visiting professor at the University of Sunderland (UK), University of Maryland (USA), and Chongqing University in China.

Summary:

We'll explore how to harness the power of digital twins to revolutionize O&M, with a focus on vibrations. This PLM-driven strategy promises to boost predictive maintenance, enhance asset performance, and minimize downtime.

• Data Collection and Integration: Begin by outfitting assets with vibration sensors and integrating them with your PLM system for seamless data flow.
• Data Management: Collect and store vibration data systematically in a central database, ensuring it's timestamped and well-structured.
• Digital Twin Creation: Utilize this data to create digital twins for each asset. These twins mimic the physical asset's geometry, behavior, and operational history.
• Simulation and Modeling: Develop simulation capabilities in your PLM system to replicate asset behavior based on vibration data. This involves creating mathematical models to predict performance under varying conditions.
• Monitoring and Analysis: Continuously monitor real-time vibration data against the digital twin's predictions. Use machine learning and analytics to spot anomalies and trends.
• Predictive Maintenance: Implement proactive maintenance strategies based on digital twin insights. Schedule maintenance when the twin foresees issues or when abnormal vibrations occur.
• Performance Optimization: Optimize asset performance by experimenting with different parameters within the digital twin before applying changes to the physical asset.
• Data Visualization and Reporting: Create dashboards within the PLM system for easy visualization of vibration data, predictions, and recommendations.
• Feedback Loop: Establish a feedback loop between the digital twin and the physical asset to improve prediction accuracy over time.
• Integration with Maintenance Systems: Seamlessly connect the PLM-based digital twin with your maintenance management system for streamlined workflows.
• Scalability and Updates: Plan for scalability as your organization expands and regularly update digital twin models to adapt to changing asset behavior.

By embracing this PLM approach for digital twins driven by vibrations, organizations can enhance asset reliability, reduce maintenance costs, and optimize overall operational efficiency. This shift from reactive to proactive maintenance empowers data-driven decision-making for assets, promising a brighter future for O&M.

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