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Course Outline
Introduction to Predictive Maintenance
- Defining predictive maintenance
- Comparing reactive, preventive, and predictive approaches
- Real-world ROI analysis and industry case studies
Data Collection and Preparation
- Sensors, IoT connectivity, and data logging in industrial contexts
- Data cleaning and structuring for analytical purposes
- Handling time series data and labeling failure events
Machine Learning for Predictive Maintenance
- Overview of machine learning models (regression, classification, anomaly detection)
- Selecting appropriate models for equipment failure prediction
- Model training, validation, and performance evaluation metrics
Developing the Predictive Workflow
- End-to-end pipeline: data ingestion, analysis, and alert generation
- Leveraging cloud platforms or edge computing for real-time analysis
- Integration with existing CMMS or ERP systems
Failure Mode and Health Index Modeling
- Predicting specific failure modes
- Calculating Remaining Useful Life (RUL)
- Creating asset health dashboards
Visualization and Alerting Systems
- Visualizing predictions and operational trends
- Establishing thresholds and configuring alerts
- Designing actionable insights for operational staff
Best Practices and Risk Management
- Addressing data quality challenges
- Ethics and explainability in industrial AI systems
- Change management and organizational adoption
Summary and Next Steps
Requirements
- Familiarity with industrial equipment and maintenance processes
- Basic understanding of AI and machine learning principles
- Experience with data collection and monitoring systems
Target Audience
- Maintenance engineers
- Reliability engineering teams
- Operations managers
14 Hours
