AI-Powered Predictive Maintenance for Industrial Systems Training Course

Fundamentals of Predictive Maintenance

• Defining predictive maintenance.
• Comparing reactive, preventive, and predictive methods.
• Real-world ROI and sector-specific case studies.

Data Acquisition and Preparation

• Sensors, IoT, and data logging in industrial contexts.
• Preparing and structuring data for analytical use.
• Handling time-series data and labelling failure events.

Machine Learning in Predictive Maintenance

• Survey of machine learning models (regression, classification, anomaly detection).
• Selecting appropriate models for equipment failure forecasting.
• Training, validating models, and assessing performance indicators.

Constructing the Predictive Workflow

• End-to-end pipeline: data ingestion, processing, and alert generation.
• Utilising cloud platforms or edge computing for real-time analysis.
• Integrating with current CMMS or ERP systems.

Failure Mode and Health Index Modelling

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

Visualisation and Alerting Mechanisms

• Displaying predictions and trends.
• Establishing thresholds and triggering alerts.
• Crafting actionable insights for operators.

Best Practices and Risk Mitigation

• Addressing data quality challenges.
• Ethics and interpretability in industrial AI systems.
• Managing change and adoption across teams.

Recap and Future Steps