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 metrics and industry case studies

Data Acquisition and Preparation

• Utilising sensors, IoT, and data logging in industrial settings
• Cleaning and structuring data for effective analysis
• Handling time series data and failure labelling

Machine Learning for Predictive Maintenance

• Overview of machine learning models including regression, classification, and anomaly detection
• Selecting appropriate models for predicting equipment failure
• Model training, validation processes, and performance metrics

Constructing the Predictive Workflow

• Establishing end-to-end pipelines: data ingestion, analysis, and alert generation
• Leveraging cloud platforms or edge computing for real-time analysis
• 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 identifying trends
• Configuring thresholds and generating alerts
• Designing actionable insights for operators

Best Practices and Risk Management

• Addressing data quality challenges
• Ethics and explainability within industrial AI systems
• Managing change and driving adoption across teams

Summary and Next Steps