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.

Hums & digital twins challenges

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 structural integrity or the severity of mechanical environments.
Predict residual service life given a given mission profile
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

For more information please download our dedicated HUMS and structural reliability white paper.

Hums & digital twins specifications

How to make the difference?

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

Download our white paper

In almost all industrial sectors reliability requirements are becoming more and more stringent and “time to market” intervals are increasingly shorter, leading Metravib to develop and offer methods for supporting our clients to validate and guarantee the reliability of their products during all the phases of their lifetime, from design to operations.
This white book gives you more insight into field proven approaches and methods.