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Duration 14 hours
Course Outline
Introduction to Predictive Maintenance
- Defining predictive maintenance
- Comparing reactive, preventive, and predictive approaches
- Real-world ROI analysis and industry case studies
Data Acquisition and Preparation
- Sensors, IoT, and data logging in industrial contexts
- Data cleansing and structuring for analytical purposes
- Time series analysis and failure labeling
Machine Learning Applications in Predictive Maintenance
- Overview of ML models (regression, classification, anomaly detection)
- Selecting optimal models for equipment failure prediction
- Model training, validation, and performance evaluation
Constructing the Predictive Workflow
- End-to-end pipelines: data ingestion, analysis, and alerting
- 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)
- Creating asset health dashboards
Visualization and Alerting Mechanisms
- Visualizing predictive trends and outcomes
- Configuring 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 systems
- Change management and team adoption strategies
Conclusion and Future Directions
Requirements
- Familiarity with industrial equipment and maintenance processes
- Basic knowledge of AI and machine learning principles
- Experience with data acquisition and monitoring systems
Target Audience
- Maintenance engineers
- Reliability specialists
- Operations managers
Testimonials (1)
How to use and explore AI for daily works
