AI-Powered Predictive Maintenance for Industrial Systems Training Course

Introduction to Predictive Maintenance

• Defining predictive maintenance and its core principles
• Comparing reactive, preventive, and predictive approaches
• Analyzing real-world ROI and industry case studies

Data Collection and Preparation

• Utilizing sensors, IoT, and data logging in industrial settings
• Cleaning and structuring data for effective analysis
• Handling time series data and failure labeling techniques

Machine Learning for Predictive Maintenance

• Overview of key ML models: regression, classification, and anomaly detection
• Selecting appropriate models for equipment failure prediction
• Training, validating, and evaluating model performance metrics

Building the Predictive Workflow

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

Failure Mode and Health Index Modeling

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

Visualization and Alerting Systems

• Visualizing predictive trends and patterns
• Configuring thresholds and generating automated alerts
• Designing actionable insights for operators

Best Practices and Risk Management

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

Summary and Next Steps