Day 1: Introduction to Predictive Maintenance and Critical Assets

• Concept and importance of predictive maintenance

• Definition and types of critical assets in industrial and engineering facilities

• Differences between predictive, preventive, and corrective maintenance

• Objectives and guarantees for operational continuity

• Basic tools and technologies for predictive maintenance

Day 2: Sensing Technologies and Data Acquisition

• Types of sensors used (vibration, temperature, pressure, acoustic)

• Remote and field sensing technologies

• Real-time data acquisition systems

• Data integration from multiple sources

• Challenges in data collection and analysis

Day 3: Data Analysis and Diagnostics

• Vibration analysis and early fault detection

• Thermal and acoustic data analysis

• Machine learning and AI techniques in data analysis

• Fault prediction models and optimal maintenance intervals

• Software applications for analysis

Day 4: Planning and Implementing Predictive Maintenance

• Maintenance planning strategies based on predictions

• Resource management and maintenance scheduling

• Coordination between operations and maintenance teams

• Evaluating the effectiveness of predictive maintenance

• Reporting techniques and continuous monitoring

Day 5: Ensuring Operational Continuity and Performance Improvement

• Risk management of critical assets

• Emergency procedures and rapid response plans

• Enhancing asset performance using predictive maintenance data

• Successful case studies

• Workshop: Developing a comprehensive predictive maintenance plan for critical assets