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Course Outline
Introduction to Predictive Maintenance
- Defining predictive maintenance
- Comparing reactive, preventive, and predictive approaches
- Real-world return on investment (ROI) and industry case studies
Data Collection and Preparation
- Sensors, IoT connectivity, and data logging in industrial contexts
- Cleaning and structuring data for analytical purposes
- Handling time series data and labeling failure events
Machine Learning Applications in Predictive Maintenance
- Overview of relevant 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
- Constructing an end-to-end pipeline: data ingestion, analysis, and alert generation
- Leveraging cloud platforms or edge computing for real-time analysis
- Integrating with existing CMMS or ERP systems
Failure Mode and Health Index Modeling
- Forecasting specific failure modes
- Estimating Remaining Useful Life (RUL)
- Creating asset health dashboards
Visualization and Alerting Systems
- Visualizing predictions and operational trends
- Setting thresholds and generating alerts
- Designing actionable insights for operational staff
Best Practices and Risk Management
- Addressing data quality challenges
- Ethics and explainability in industrial AI applications
- Managing change and fostering adoption across teams
Summary and Next Steps
Requirements
- Familiarity with industrial equipment and maintenance procedures
- Basic understanding of AI and machine learning principles
- Experience with data acquisition and monitoring systems
Target Audience
- Maintenance engineers
- Reliability engineering teams
- Operations managers
14 Hours
