This presentation delves into the evolution of enabling technologies driving reliability and shaping Industry 4.0, emphasizing their impact on modern maintenance and asset management practices.

• Introduction to Reliability in Industry 4.0: The integration of cutting-edge technologies enhances the efficiency, accuracy, and sustainability of maintenance processes.
• Text Recognition and AI in CMMS: Explore how Artificial Intelligence (AI) automates data entry in Computerized Maintenance Management Systems (CMMS), minimizing errors and saving time. Additionally, generative AI is revolutionizing the creation of maintenance reports, delivering consistent and professional documentation with minimal effort.
• Advanced Visual Inspection: Use of drones and robots for inspections. These tools enable visual, thermographic, and ultrasonic (US) inspections, particularly in hazardous or hard-to-reach environments, ensuring higher safety standards and precision.
• Data Analytics for Proactive Maintenance: Harness the power of machine learning and big data analytics to interpret complex datasets, such as noise and vibration, for advanced anomaly detection. These insights support predictive maintenance, minimizing downtime and optimizing asset reliability.

The presentation concludes with an overview of the benefits of these technologies, underscoring their pivotal role in improving reliability and efficiency in asset management within the framework of Industry 4.0.

María José Gómez García, a distinguished professional in the field of predictive maintenance and reliability engineering. As a key member of the team at PREDICTEC, María José has been instrumental in driving innovative solutions that enhance the efficiency and reliability of industrial operations. Her expertise lies in leveraging advanced technologies to monitor equipment condition, anticipate potential failures, and optimize asset performance.

María José’s work reflects a deep commitment to advancing Industry 4.0 principles, integrating tools such as data analytics and intelligent monitoring systems to create smarter, more sustainable maintenance practices. Her contributions have not only improved operational reliability for numerous organizations but also underscored the importance of predictive strategies in modern asset management.

Beyond her professional endeavors, María José is an active participant in technical forums and conferences, where she shares her insights on the evolving landscape of reliability and predictive maintenance. Her dedication to the field makes her a valuable voice in the ongoing conversation about the future of industrial innovation.

Jesús Puebla Guedea is an engineer specialized in reliability and predictive maintenance, currently playing a key role at PREDICTEC, where he focuses on the development of strategies and services aimed at predictive maintenance. His expertise lies in optimizing industrial equipment performance through early failure detection, helping to reduce operational costs and minimize downtime. Jesús is an active contributor to the field of condition-based maintenance, integrating cutting-edge technologies such as data analytics and machine learning within the Industry 4.0 framework. His work emphasizes practical applications, providing companies with efficient solutions to improve asset reliability and sustainability.

In addition to his professional achievements, Jesús is a frequent speaker at specialized conferences, sharing his insights on implementing predictive maintenance programs and advancing reliability engineering. His contributions reflect a deep understanding of the challenges and opportunities in managing critical systems.