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Duration 14 hours
Course Outline
Fundamentals of Predictive Maintenance
- Defining predictive maintenance
- Comparing reactive, preventive, and predictive approaches
- Real-world ROI analysis and industry case studies
Data Acquisition and Preparation
- Utilizing sensors, IoT, and data logging in industrial contexts
- Cleaning and structuring data for effective analysis
- Handling time series data and failure labeling techniques
Machine Learning in Predictive Maintenance
- Overview of ML models: regression, classification, and anomaly detection
- Selecting optimal models for equipment failure prediction
- Model training, validation processes, and performance metrics
Constructing Predictive Workflows
- Building end-to-end pipelines: data ingestion, analysis, and alerting
- Leveraging cloud platforms or edge computing for real-time processing
- Integrating solutions with existing CMMS or ERP systems
Failure Mode and Health Index Modeling
- Forecasting specific failure modes
- Computing Remaining Useful Life (RUL)
- Creating asset health dashboards for monitoring
Visualization and Alerting Mechanisms
- Visualizing predictive trends and outcomes
- Configuring thresholds and generating alerts
- Designing practical insights for on-site operators
Best Practices and Risk Mitigation
- Addressing data quality challenges
- Ethics and explainability in industrial AI implementations
- Change management and team adoption strategies
Key Takeaways and Future Actions
Requirements
- Solid grasp of industrial equipment and standard maintenance procedures
- Fundamental knowledge of AI and machine learning principles
- Background in data acquisition and monitoring systems
Target Audience
- Maintenance Engineers
- Reliability Teams
- Operations Managers
Testimonials (1)
How to use and explore AI for daily works
