AI-Powered Predictive Maintenance for Industrial Systems Training Course

Introduction to Predictive Maintenance

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

Data Collection and Preparation

• Utilizing sensors, IoT, and data logging in industrial contexts.
• Cleaning and structuring data for analytical purposes.
• Handling time series data and labeling failure events.

Machine Learning for Predictive Maintenance

• Overview of ML models (regression, classification, anomaly detection).
• Selecting appropriate models for equipment failure prediction.
• Training, validating, and evaluating model performance.

Building the Predictive Workflow

• Constructing end-to-end pipelines: 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 Modeling

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

Visualization and Alerting Systems

• Visualizing predictions and emerging trends.
• Defining thresholds and establishing alert mechanisms.
• Formulating actionable insights for operators.

Best Practices and Risk Management

• Addressing data quality challenges.
• Ethics and explainability in industrial AI applications.
• Managing change and fostering team adoption.

Summary and Future Steps