AI-Powered Predictive Maintenance for Industrial Systems Training Course

Introduction to Predictive Maintenance

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

Data Collection and Preparation

• Sensors, IoT, and data logging in industrial settings
• Preparing and structuring data for analysis
• Handling time series data and labeling failures

Machine Learning for Predictive Maintenance

• Survey of machine learning models (regression, classification, anomaly detection)
• Selecting optimal models for predicting equipment failure
• Training models, validation, and assessing performance metrics

Constructing the Predictive Workflow

• Building end-to-end pipelines: data ingestion, analysis, and alerting
• Utilizing cloud platforms or edge computing for real-time processing
• 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

• Displaying predictions and trends
• Configuring thresholds and generating alerts
• Formulating actionable insights for operators

Best Practices and Risk Management

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

Conclusion and Future Steps