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Course Outline

Introduction to Predictive Maintenance

  • Understanding predictive maintenance
  • Comparing reactive, preventive, and predictive approaches
  • Real-world return on investment and industry case studies

Data Collection and Preparation

  • Sensors, IoT, and data logging in industrial environments
  • Time series data and failure labeling

Machine Learning for Predictive Maintenance

  • Overview of machine learning models (regression, classification, anomaly detection)

Building the Predictive Workflow

  • End-to-end pipeline: data ingestion, analysis, and alerts
  • Using cloud platforms or edge computing for real-time analysis
  • Integration with existing CMMS or ERP systems

Failure Mode and Health Index Modeling

  • Predicting specific failure modes
  • Calculating Remaining Useful Life (RUL)
  • Developing asset health dashboards

Visualization and Alerting Systems

  • Visualizing predictions and trends
  • Setting thresholds and creating alerts
  • Designing actionable insights for operators

Best Practices and Risk Management

  • Overcoming data quality issues
  • Change management and adoption across teams

Summary and Next Steps

Requirements

  • Knowledge of industrial equipment and maintenance workflows

Audience

 14 Hours

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