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Course Outline
Introduction to Predictive Maintenance
- What is predictive maintenance?
- Reactive vs. preventive vs. predictive approaches
- Real-world ROI and industry case studies
Data Collection and Preparation
- Sensors, IoT, and data logging in industrial environments
- Data cleaning and structuring for analysis
- Time series data and failure labeling
Machine Learning for Predictive Maintenance
- Overview of machine learning models (regression, classification, anomaly detection)
- Choosing the right model for equipment failure prediction
- Model training, validation, and performance metrics
Building the Predictive Workflow
- End-to-end pipeline: data ingestion, analysis, and alerts
- Using 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)
- Developing asset health dashboards
Visualization and Alerting Systems
- Visualizing predictions and trends
- Setting thresholds and creating alerts
- Designing actionable insights for operators
Best Practices and Risk Management
- Overcoming data quality issues
- Ethics and explainability in industrial AI systems
- Change management and adoption across teams
Summary and Next Steps
Requirements
- Understanding of industrial equipment and maintenance workflows
- Basic familiarity with AI and machine learning concepts
- Experience with data collection and monitoring systems
Audience
- Maintenance engineers
- Reliability teams
- Operations managers
14 Hours
