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Machine Health Indicators and Digital Twins.

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  • 1BGU-PHM Laboratory, Department of Mechanical Engineering, Ben-Gurion University of the Negev, P.O. Box 653, Beer Sheva 8410501, Israel.

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Summary
This summary is machine-generated.

This study integrates health indicators (HIs) with digital twins (DTs) for advanced system monitoring and predictive maintenance. This fusion enhances remaining useful life (RUL) estimation and failure prediction in engineering systems.

Keywords:
AIcondition-based maintenance (CBM)digital twins (DTs)health indicators (HIs)prognostics and health managementsensorsstructural health monitoring

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Area of Science:

  • Engineering
  • Computer Science
  • Data Science

Background:

  • Health indicators (HIs) quantify system condition using sensor data for diagnostics and prognostics.
  • Digital twins (DTs) are virtual replicas of physical assets, enhancing monitoring and analytics.
  • Integrating HIs and DTs is crucial for advancing predictive maintenance and structural health monitoring.

Purpose of the Study:

  • To explore the synergistic integration of health indicators and digital twins.
  • To illustrate their application in condition-based maintenance and structural health monitoring.
  • To discuss methodologies for anomaly detection and failure prediction in rotary machinery.

Main Methods:

  • Utilizing data-driven and physics-based approaches for system analysis.
  • Applying advanced modeling and machine learning (ML) techniques.
  • Deriving health indicators from vibration analysis and soft sensors.

Main Results:

  • Demonstrated the capability of HIs and DTs to detect anomalies and predict failures.
  • Showcased applications in rotary machinery, specifically bearings and gears.
  • Highlighted the role of hybrid modeling and uncertainty quantification for improved health monitoring.

Conclusions:

  • The integration of HIs and DTs significantly advances performance engineering and predictive maintenance.
  • Addressing challenges in data labeling and uncertainty quantification is key for future development.
  • Digital twins are pivotal tools for enhancing the reliability and efficiency of engineering systems.