Day 1: Introduction to Predictive Maintenance and Artificial Intelligence

• Concept and importance of predictive maintenance

• Differences between preventive, predictive, and corrective maintenance

• Principles of AI and machine learning in maintenance

• Benefits of AI applications in engineering maintenance

• Challenges and opportunities in predictive maintenance

Day 2: Data Collection and Analysis for Predictive Maintenance

• Types of data used (vibration, temperature, sound, etc.)

• Sensing technologies and Internet of Things (IoT)

• Data collection and preprocessing methods

• Data mining and cleaning

• Building asset and equipment databases

Day 3: AI Models for Predictive Maintenance

• Basic machine learning techniques

• Regression and classification models

• Deep neural networks

• Time series analysis

• Failure prediction models

Day 4: AI Applications in Predictive Maintenance

• Early fault detection

• Predicting optimal maintenance intervals

• Inventory and spare parts management using AI

• Integration with Computerized Maintenance Management Systems (CMMS)

• Successful case studies across engineering sectors

Day 5: Future Challenges and Emerging Technologies

• Cloud computing and big data in predictive maintenance

• Explainable AI (XAI)

• Integration with robotics and intelligent control systems

• Sustainability, cost reduction, and operational resilience

• Workshop: Designing a practical predictive maintenance model