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Course Outline
Fundamentals of Predictive Maintenance
- Defining predictive maintenance
- Real-world ROI and industry case studies
Data Acquisition and Preparation
- Sensors, IoT, and data logging within industrial contexts
- Cleaning and structuring data for analytical purposes
- Time series analysis and failure labeling
Machine Learning in Predictive Maintenance
- Overview of ML models (regression, classification, anomaly detection)
- Selecting appropriate models for predicting equipment failures
Constructing the Predictive Workflow
- End-to-end pipelines: data ingestion, analysis, and alerting
- Leveraging cloud platforms or edge computing for real-time processing
- Integration with existing CMMS or ERP systems
Modeling Failure Modes and Health Indices
- Forecasting specific failure modes
- Calculating Remaining Useful Life (RUL)
- Developing asset health dashboards
Visualization and Alert Systems
- Displaying predictions and trends
- Configuring thresholds and generating alerts
- Creating actionable insights for operators
Best Practices and Risk Management
- Addressing data quality challenges
- Ethics and explainability in industrial AI deployments
- Change management and team adoption strategies
Recap and Future Actions
Requirements
- Familiarity with industrial machinery and maintenance processes
- Experience with data acquisition and monitoring frameworks
Target Audience
- Maintenance engineers
- Reliability specialists
- Operations managers
14 Hours