AI-Powered Predictive Maintenance for Industrial Systems Training Course

Foundations of Predictive Maintenance

• Defining predictive maintenance
• Comparing reactive, preventive, and predictive methodologies
• Examining real-world ROI and industry examples

Data Acquisition and Preprocessing

• Leveraging sensors, IoT, and data logging in industrial contexts
• Cleaning and structuring data for effective analysis
• Handling time series data and labeling failure events

Machine Learning Applications in Predictive Maintenance

• Reviewing ML models: regression, classification, and anomaly detection
• Selecting appropriate models for forecasting equipment failures
• Training, validating models, and evaluating performance metrics

Constructing the Predictive Workflow

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

Failure Mode and Health Index Modelling

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

Visualisation and Alert Systems

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

Best Practices and Risk Mitigation

• Addressing data quality challenges
• Ensuring ethics and explainability in industrial AI
• Managing change and driving adoption across teams

Recap and Future Directions