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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

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