🧠 General Introduction

In the era of rapid digital transformation, industrial facilities are increasingly adopting artificial intelligence (AI) to optimize operations and reduce costs. Smart maintenance represents a paradigm shift in asset management, leveraging data analytics and predictive algorithms to anticipate failures before they occur.

This proactive approach enhances operational efficiency, minimizes unplanned downtime, and extends equipment lifespan. This workshop offers participants a comprehensive understanding of how AI can be integrated into maintenance strategies, showcasing cutting-edge tools and technologies.

It highlights real-world applications and successful case studies from global industrial environments, providing a clear vision of the future of smart maintenance.

Participants will learn how to analyze industrial data, apply machine learning techniques, and implement Internet of Things (IoT) solutions to build intelligent maintenance systems.

Through interactive sessions and hands-on exercises, attendees will gain the skills needed to design and deploy AI-powered maintenance solutions aligned with Industry 4.0 standards.

🎯 Target Audience

• Maintenance and operations managers in industrial facilities
• Mechanical, electrical, and control engineers
• Industrial data analysts
• Digital transformation and technology leaders
• Academics and researchers in industrial AI
• Tech entrepreneurs in industrial innovation

🎯 Expected Outcomes

• Understand the fundamentals of smart maintenance and AI
• Learn how to collect and analyze industrial data
• Acquire skills in predictive maintenance and performance optimization
• Apply AI models in real industrial scenarios
• Design a comprehensive smart maintenance strategy

🧪 Scientific Topics:

Track 1: Fundamentals of Smart Maintenance and AI

Session 1: Introduction to Smart Maintenance

• Definition and scope of smart maintenance
• Traditional vs. intelligent maintenance approaches
• Benefits of adopting smart maintenance

Session 2: Basics of Artificial Intelligence in Industry

• Key AI and machine learning concepts
• Industrial applications of AI
• Challenges and opportunities in AI adoption

Track 2: Industrial Data Analytics

Session 1: Data Collection from Industrial Equipment

• Types of industrial data
• Sensor technologies and IoT integration
• Data acquisition challenges

Session 2: Data Processing and Analysis

• Data analytics tools
• Cleaning and classification techniques
• Key performance indicators for maintenance

Track 3: Predictive Maintenance and Performance Optimization

Session 1: Predictive Failure Models

• Algorithms for fault prediction
• Real-world case studies
• Model accuracy and validation

Session 2: Enhancing Operational Performance with AI

• Continuous improvement techniques
• Reducing unplanned downtime
• Risk management strategies

Track 4: AI Applications in Maintenance

Session 1: Predictive Maintenance Systems

• Preventive vs. predictive maintenance
• Tools for predictive maintenance
• Integration with asset management systems

Session 2: Industrial Case Studies

• Successful implementations
• Results analysis and challenges
• Lessons learned from global facilities

Track 5: Designing a Smart Maintenance Strategy

Session 1: Building a Smart Maintenance Roadmap

• Current state assessment
• Goal setting and KPI selection
• Technology selection and planning

Session 2: Change Management and Digital Transformation

• Leading change within the organization
• Training technical teams
• Measuring impact and ROI