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Projects

Fields of application

Ezako has been involved in many projects with leading industrial and defense companies as Thales, Naval Group, MBDA, Safran Aircraft Engines, CNES and ANFR. All projects start with the same attention to data quality and labeling.

Projects

References

  1. DeepGreen

    Vibration monitoring on an embedded board

    Research and development within DeepGreen, the project led by CEA that builds the open-source Aidge platform for embedded AI: fault detection by machine learning on a microcontroller board of the STM32 class. Power generation machines were fitted with sensors to detect a departure from their expected behaviour; Ezako brings its expertise in time series.

  2. Safran Aircraft Engines

    Abnormal observations in engine test signals

    A deep learning method to detect abnormal observations in the high-frequency sensor series of engine development tests, where they make the spectrograms unusable. Published with Safran Aircraft Engines.

  3. CNES

    Anomalies in satellite telemetry

    Detection of anomalies in satellite telemetry by machine learning, in addition to the conventional surveillance methods.

  4. ANFR

    Which radio sites to inspect first

    Prediction of the radio sites most likely to reveal an anomaly, to set the order of the inspections of a national park of more than 76,000 sites.

  5. DGA

    Neural networks across underwater platforms

    A development project to adapt neural networks to each underwater platform (drones, buoys, hydrophones, sonars) and to improve the real-time detection of underwater anomalies.

  6. Alseamar

    Deep learning on autonomous underwater platforms

    Carried out with Alseamar within the RAPID project DASMIA.

  7. MION

    Dangerous and illegal substances in luggage

    VASCREEN, developed with MION within the SecurIT programme, to detect potentially dangerous and illegal substances in luggage and goods.

Other fields of application

  • Rotating machines and turbines

    Detecting a drift in vibration, pressure and temperature series before it becomes a failure.

  • Production lines

    Spotting a quality drift in the sensors of a line before it reaches the parts.

  • Cables, pipelines and perimeters

    Monitoring a route by distributed acoustic sensing: vessel traffic, marine biology, perimeter intrusion.

  • Fleets of equipment

    Ranking which sites, charging stations or vehicles to inspect first, as for ANFR’s radio sites.

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