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Course Outline
Overview of Predictive Maintenance
- Definition and scope of predictive maintenance
- Comparison of reactive, preventive, and predictive strategies
- Measured return on investment and government sector case studies
Data Acquisition and Preparation
- Utilization of sensors, IoT infrastructure, and logging systems in operational environments
- Data sanitization and structuring for analytical processing
- Management of time series data and classification of failure events
Application of Machine Learning for Predictive Maintenance
- Review of applicable machine learning models, including regression, classification, and anomaly detection
- Selection criteria for models targeting equipment failure prediction
- Procedures for model training, validation, and performance evaluation
Development of Predictive Workflows
- Implementation of end-to-end pipelines encompassing data ingestion, analysis, and notification systems
- Deployment of cloud-based or edge computing solutions for real-time analytics
- Integration with existing Computerized Maintenance Management Systems (CMMS) or Enterprise Resource Planning (ERP) platforms
Failure Mode and Asset Health Index Modeling
- Identification and prediction of specific failure modes
- Calculation of Remaining Useful Life (RUL)
- Creation of asset health monitoring dashboards for agency use
Visualization and Notification Systems
- Display of predictive outcomes and operational trends
- Establishment of thresholds and generation of automated alerts
- Formulation of actionable guidance for maintenance personnel
Best Practices and Risk Mitigation
- Strategies for addressing data quality challenges
- Ethical considerations and algorithmic explainability in industrial AI applications
- Change management protocols to facilitate adoption across departments
Summary and Future Directions
Requirements
- Knowledge of industrial equipment maintenance procedures and operational workflows
- Foundational understanding of artificial intelligence and machine learning principles
- Background in data acquisition and monitoring infrastructure
Target Audience
- Maintenance engineering staff
- Reliability assurance groups
- Operations management personnel
14 Hours
