Course Details

This course is appropriate for a wide range of professionals but not limited to:

• Maintenance Engineers and Technicians
• Maintenance Managers and Supervisors
• Reliability Engineers
• Plant and Facility Managers
• Asset Management Professionals
• Industrial Engineers
• Technical professionals involved in digital transformation or Industry 4.0 initiatives
• Anyone interested in understanding how AI can enhance maintenance performance

• Expert-led sessions with dynamic visual aids
• Comprehensive course manual to support practical application and reinforcement
• Interactive discussions addressing participants’ real-world projects and challenges
• Insightful case studies and proven best practices to enhance learning

By the end of this course, participants will be able to:

• Understand the fundamentals of Artificial Intelligence, Machine Learning, and Data Science.
• Identify the key AI applications in maintenance, such as predictive maintenance and anomaly detection.
• Learn how to collect, preprocess, and analyze equipment data for AI-driven maintenance decisions.
• Evaluate AI tools and platforms suitable for maintenance environments.
• Design and implement basic AI models for maintenance use cases.
• Interpret results from AI systems and support data-driven decision-making.
• Explore real-world industry use cases and success stories in AI for maintenance.
• Understand the challenges and limitations of applying AI in maintenance operations.

DAY 1

Introduction to AI and Industry 4.0

• Welcome and Introduction
• Pre-test
• Overview of Industry 4.0 and Smart Maintenance
• Introduction to Artificial Intelligence, Machine Learning & Data Science
• Types of AI: Supervised, Unsupervised, and Reinforcement Learning
• Role of AI in modern maintenance strategies
• Case study

DAY 2

Data in Maintenance

• Importance of data in predictive and prescriptive maintenance
• Data types: sensor, machine logs, environmental, operational
• Data collection tools (IoT, SCADA, PLCs, CMMS)
• Data preprocessing and cleansing techniques
• Data visualization for maintenance analysis
• Case study

DAY 3

AI Techniques for Maintenance

• Predictive maintenance vs. preventive maintenance
• AI techniques: classification, regression, clustering, anomaly detection
• Machine learning models: decision trees, SVM, neural networks
• Introduction to time series analysis and forecasting equipment failure
• Case study

DAY 4

Tools, Platforms & Integration

• Overview of AI platforms: Python, MATLAB, Azure ML, IBM Maximo, etc.
• Integrating AI with existing maintenance systems (ERP, CMMS, EAM)
• Edge AI and real-time monitoring
• Cloud-based vs. on-premise AI systems
• Cybersecurity considerations in AI-driven maintenance
• Group Exercise

DAY 5

Applications, ROI, and Future Trends

• Real-world use cases: Energy, Manufacturing, Oil & Gas, Utilities
• Measuring ROI for AI in maintenance
• Change management and team readiness
• Challenges in implementing AI (data quality, resistance to change, cost)
• Future trends: Digital twins, autonomous maintenance, and generative AI
• Final Project
• Post-test
• Certificate Ceremony