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Course Outline
Introduction to Predictive Maintenance
- Defining predictive maintenance
- Comparing reactive, preventive, and predictive strategies
- Real-world ROI analysis and industry case studies
Data Collection and Preparation
- Sensors, IoT integration, and data logging in industrial contexts
- Cleaning and structuring data for analysis
- Handling time series data and labeling failure events
Machine Learning for 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
Constructing the Predictive Workflow
- End-to-end pipeline: data ingestion, analysis, and alert generation
- Utilizing cloud platforms or edge computing for real-time processing
- Integrating with existing CMMS or ERP systems
Failure Mode and Health Index Modeling
- Forecasting specific failure modes
- Calculating Remaining Useful Life (RUL)
- Developing asset health dashboards
Visualization and Alerting Systems
- Visualizing predictions and trends
- Establishing thresholds and configuring alerts
- Designing actionable insights for operational teams
Best Practices and Risk Management
- Addressing data quality challenges
- Ethical considerations and explainability in industrial AI
- Managing change and driving adoption across teams
Summary and Next Steps
Requirements
- Familiarity with industrial equipment and maintenance processes
- Basic knowledge of AI and machine learning principles
- Experience with data collection and monitoring infrastructure
Target Audience
- Maintenance engineers
- Reliability specialists
- Operations managers
14 Hours
