AI-Powered Predictive Maintenance for Industrial Systems Training Course

Introduction to Predictive Maintenance

• Defining predictive maintenance
• Comparing reactive, preventive, and predictive approaches
• Real-world ROI analysis and industry examples

Data Acquisition and Preparation

• Sensors, IoT, and data logging in industrial contexts
• Preparing and structuring data for analysis
• Handling time-series data and failure labelling

Applying Machine Learning to Predictive Maintenance

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

Developing the Predictive Workflow

• End-to-end pipeline design: data ingestion, analysis, and alerting
• Leveraging cloud platforms or edge computing for real-time processing
• Integrating with existing CMMS or ERP systems

Failure Mode and Health Index Modelling

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

Visualisation and Alerting Systems

• Visualising predictions and trends
• Configuring thresholds and generating alerts
• Designing actionable insights for operators

Best Practices and Risk Management

• Addressing data quality challenges
• Ethics and explainability in industrial AI systems
• Change management and team adoption

Summary and Next Steps