man making measurements in factory

Structural Health Monitoring & Digital Twins

“Monitor health and usage of a structure or an equipment from dedicated measurements and tools to improve its reliability.”

HUMS & Digital Twins goals

Ensure the reliability of your products in operation

The life cycle of a product begins with the design phase and ends at its operational phase. 

The requirements for more and more reliability and the increasingly short time to market durations led Metravib to develop and propose methods to support its customers in order to validate and guarantee the reliability of their products following all V cycle phases.

Reliability is nowadays increasingly controlled from design to validation. However it often leads to oversizing the equipment or structures according to the risk of failure accepted. It does not always manage properly the great variability of uses or mechanical environment.

jean yves disson lead engineer noise and vibration expert

HUMS & Digital Twins challenges

man monitoring health of machine structure

Health structure monitoring

Usage monitoring and health monitoring of structure exposed to severe mechanical environments is not so usual during operational phases.

However, a proper HUMS strategy leads to:

  • Reduce the uncertainty on reliability by controlling usages and the environment
  • Provide maintenance assistance by monitoring the structure health status
  • Simplify and reduce the need for instrumentation by using machine learning tools to build a virtual sensor, a digital twin or a prediction model of the health status of the monitored structure

HUMS & Digital Twins specifications

How to make the difference?

monitoring of multi-physics measurements data
  • Monitoring from multi-physics measurements data acquired 24/7 in easily instrumentable areas
  • Health status obtained from a physical model of the structure
  • Development of a meta-model or digital twin trained to predict the health status, possibly using multiple measurement campaigns
  • Meta-model training based on machine learning methods
  • Generic methods applicable to all structures

HUMS & Digital Twins applications

  • Classification of usages for vehicles in transit
  • Monitoring the health status of underwater structures from surface measurements
  • Design of railway equipment box subject to variable loads, vibrations and shocks
  • Specification of an endurance test for electrical equipment mounted on a vehicle from its actual environment
  • Monitoring the main organs of a wind turbine
  • Monitoring of the state of health of pipelines
  • Fault detection
wind turbine under construction


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