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Duration 14 hours
Course Outline
Introduction to Predictive Maintenance
- Defining predictive maintenance
- Comparing reactive, preventive, and predictive methodologies
- Real-world ROI analysis and industry examples
Data Acquisition and Preparation
- Sensors, IoT, and data logging in industrial contexts
- Cleaning and structuring data for effective analysis
- Time-series data management and failure labeling
Machine Learning in Predictive Maintenance
- Overview of ML models (regression, classification, anomaly detection)
- Selecting appropriate models for equipment failure prediction
- Model training, validation, and key performance metrics
Constructing the Predictive Workflow
- End-to-end pipeline: data ingestion, analysis, and alert generation
- Leveraging cloud platforms or edge computing for real-time insights
- 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
- Visualizing predictions and trend analysis
- Configuring thresholds and alert systems
- Crafting actionable insights for operators
Best Practices and Risk Management
- Addressing data quality challenges
- Ethics and interpretability in industrial AI systems
- Change management and team adoption strategies
Conclusion and Future Directions
Requirements
- Familiarity with industrial equipment and maintenance processes
- Fundamental knowledge of AI and machine learning principles
- Practical experience with data acquisition and monitoring systems
Target Audience
- Maintenance engineers
- Reliability specialists and teams
- Operations managers
