Formation AI-Powered Predictive Maintenance for Industrial Systems

Introduction to Predictive Maintenance

• What is predictive maintenance?
• Reactive vs. preventive vs. predictive approaches
• Real-world ROI and industry case studies

Data Collection and Preparation

• Sensors, IoT, and data logging in industrial environments
• Data cleaning and structuring for analysis
• Time series data and failure labeling

Machine Learning for Predictive Maintenance

• Overview of machine learning models (regression, classification, anomaly detection)
• Choosing the right model for equipment failure prediction
• Model training, validation, and performance metrics

Building the Predictive Workflow

• End-to-end pipeline: data ingestion, analysis, and alerts
• Using cloud platforms or edge computing for real-time analysis
• Integration with existing CMMS or ERP systems

Failure Mode and Health Index Modeling

• Predicting specific failure modes
• Calculating Remaining Useful Life (RUL)
• Developing asset health dashboards

Visualization and Alerting Systems

• Visualizing predictions and trends
• Setting thresholds and creating alerts
• Designing actionable insights for operators

Best Practices and Risk Management

• Overcoming data quality issues
• Ethics and explainability in industrial AI systems
• Change management and adoption across teams

Summary and Next Steps