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Duration 14 hours
Course Outline
Overview of Predictive Maintenance Frameworks
- Definition and scope of predictive maintenance
- Comparative analysis of reactive, preventive, and predictive strategies
- Return on investment metrics and sector-specific implementations
Data Acquisition and Structuring
- Utilization of sensors, IoT infrastructure, and industrial data logging
- Methods for data sanitization and structuring to support analytical processes
- Handling time-series data and labeling failure events
Application of Machine Learning in Asset Management
- Survey of machine learning architectures, including regression, classification, and anomaly detection
- Criteria for selecting optimal models for equipment failure forecasting
- Protocols for model training, validation, and assessment of performance metrics
Construction of Predictive Operational Workflows
- Designing comprehensive pipelines encompassing data ingestion, analytical processing, and alert generation
- Leveraging cloud services or edge computing for real-time analytical capabilities
- Integration protocols for compatibility with existing CMMS or ERP environments
Failure Mode Analysis and Health Index Development
- Forecasting specific modes of equipment failure
- Calculation and monitoring of Remaining Useful Life (RUL)
- Creation of asset health monitoring dashboards
Visualization Standards and Alerting Mechanisms
- Techniques for visualizing predictive outputs and trend analysis
- Establishing threshold criteria and configuring automated alerts
- Formulating actionable insights for operational staff
Operational Best Practices and Risk Mitigation
- Strategies for addressing data quality and integrity challenges
- Considerations regarding ethics and model explainability in industrial AI applications
- Managing organizational change and fostering adoption across teams
Summary of Key Concepts and Future Directions
Requirements
- Foundational knowledge of industrial equipment and maintenance processes
- Basic understanding of AI and machine learning principles
- Practical experience with data collection and monitoring systems
Target Audience
- Maintenance engineers
- Reliability specialists and teams
- Operations managers