AI-Powered Predictive Maintenance for Industrial Systems Training Course

Introduction to Predictive Maintenance

• Defining predictive maintenance
• Comparing reactive, preventive, and predictive approaches
• Examining real-world ROI and industry case studies

Data Collection and Preparation

• Sensors, IoT, and data logging within industrial contexts
• Cleaning and structuring data for analytical purposes
• Managing time series data and labeling failure events

Machine Learning for Predictive Maintenance

• Overview of machine learning models (regression, classification, anomaly detection)
• Selecting appropriate models for equipment failure prediction
• Model training, validation, and assessing performance metrics

Constructing the Predictive Workflow

• Building an end-to-end pipeline: data ingestion, analysis, and alerting
• Utilizing cloud platforms or edge computing for real-time analysis
• Integrating with existing CMMS or ERP systems

Failure Mode and Health Index Modeling

• Forecasting specific failure modes
• Calculating Remaining Useful Life (RUL)
• Creating asset health dashboards

Visualization and Alerting Systems

• Visualizing predictions and emerging trends
• Defining thresholds and configuring alerts
• Designing actionable insights for operators

Best Practices and Risk Management

• Addressing data quality challenges
• Ethical considerations and explainability in industrial AI systems
• Managing change and fostering adoption across teams

Summary and Next Steps