DiagFit by Amiral Technologies

DiagFit is a blind failure prediction software for IoT equipment. It enables domain experts to anticipate and plan their machine shutdowns without the need of historical failure data.

Aerospace Industrial suppliers Maintenance SaaS Defence Airlines Airports Cities

Benefits

Amiral Technologies addresses 3 main challenges of the industry market (manufacturing, energy and transport sectors) with its DiagFit software:
- A unique software for a wide variety of industrial equipment and sensors
- A specialisation in complex industrial time series treatment
- A state-of-the-art prediction of failures based on healthy data

DiagFit is no-code: no need of Data Science skills
DiagFit is agnostic: for all type of equipment and sensors
DiagFit is accurate: detection of early signs of failures
DiagFit operates in blind mode: no need of historical failure data

Blind mode: no historical failure data

No-code: no need of Data Science skills

Agnostic: all type of equipments/sensors

Detection of early signs of failures

Features

Blind failure prediction

The "blind mode" means that DiagFit does not need to know the type of equipment it is monitoring (technical or physical specifications) and does not need to be enriched with a failure history in order to operate.

Labelling of anomalies

The labelling function allows the maintenance expert to enter an anomaly in the dictionary associated with the equipment.

Designed for maintenance experts

A step-by-step process allows you to generate a predictive model dedicated to a piece of equipment without any knowledge of coding or data science. This step* called "BUILD" usually takes only a few minutes/hours.

Blind failure prediction

Labelling of anomalies

Designed for maintenance experts

They are trusting us

Customer testimony

Éric Viton

CEO of DPIA, DPIA

“To support our customers in the most restrictive environments, predictive maintenance is a real criterion of differentiation. This solution will make it possible to optimize vehicle maintenance and TCO, but also can considerably reduce safety risks.''