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Course Outline
Introduction to Predictive Maintenance
- Defining predictive maintenance
- Comparing reactive, preventive, and predictive methodologies
- Real-world ROI analysis and industry case studies
Data Collection and Preparation
- Utilizing sensors, IoT, 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 ML models (regression, classification, anomaly detection)
- Selecting appropriate models for equipment failure prediction
- Model training, validation, and performance evaluation metrics
Constructing the Predictive Workflow
- Building an end-to-end pipeline: data ingestion, analysis, and alerting
- Leveraging cloud platforms or edge computing for real-time processing
- Integrating with existing CMMS or ERP systems
Failure Mode and Health Index Modeling
- Predicting specific failure modes
- Estimating Remaining Useful Life (RUL)
- Creating asset health dashboards
Visualization and Alerting Systems
- Visualizing predictions and operational trends
- Configuring thresholds and establishing alerts
- Designing actionable insights for field operators
Best Practices and Risk Management
- Addressing data quality challenges
- Ethics and explainability in industrial AI systems
- Managing change and fostering adoption across teams
Summary and Next Steps
Requirements
- Knowledge of industrial equipment and standard maintenance procedures
- Foundational understanding of AI and machine learning principles
- Hands-on experience with data collection and monitoring tools
Target Audience
- Maintenance engineers
- Reliability engineering teams
- Operations managers
14 Hours
