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

• Utilizing sensors, IoT, and logging in industrial contexts
• Cleaning and structuring data for analytical purposes
• Managing time series data and labeling failures

Machine Learning for Predictive Maintenance

• Overview of ML models (regression, classification, anomaly detection)
• Selecting appropriate models for predicting equipment failures
• Training, validating models, and assessing performance metrics

Constructing the Predictive Workflow

• Building end-to-end pipelines for data ingestion, analysis, and alerting
• Leveraging cloud platforms or edge computing for real-time insights
• Integrating with existing CMMS or ERP systems

Failure Mode and Health Index Modeling

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

Visualization and Alerting Systems

• Visualizing predictions and emerging trends
• Establishing thresholds and generating alerts
• Crafting actionable insights for operators

Best Practices and Risk Management

• Addressing data quality challenges
• Ensuring ethics and explainability in industrial AI
• Managing change and fostering team adoption

Summary and Next Steps