AI-Powered Predictive Maintenance for Industrial Systems Training Course

Introduction to Predictive Maintenance

• Defining predictive maintenance.
• Comparing reactive, preventive, and predictive methodologies.
• Real-world ROI analysis and industry case studies.

Data Acquisition and Preparation

• Sensors, IoT, and data logging in industrial contexts.
• Cleaning and structuring data for analytical purposes.
• Handling time-series data and labeling failures.

Machine Learning for Predictive Maintenance

• Overview of ML models including regression, classification, and anomaly detection.
• Selecting appropriate models for equipment failure prediction.
• Training, validating models, and assessing performance metrics.

Constructing the Predictive Workflow

• End-to-end pipeline design: data ingestion, analysis, and alert generation.
• 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 Mechanisms

• Displaying predictions and trends.
• Configuring thresholds and generating alerts.
• Formulating actionable insights for operators.

Best Practices and Risk Management

• Addressing data quality challenges.
• Ethics and explainability in industrial AI applications.
• Managing change and promoting adoption across teams.

Summary and Future Steps