Anomaly detection with no Data Science knowledge is possible with DiagFit

Published on November 19, 2024
Articles, News
Reading time 2 min

Can anomaly detection be easily adopted by business experts with no knowledge of data analysis ? The answer is yes, thanks to DiagFit.

Can you do anomaly detection without any knowledge of Data Science ?

We talked about it in another article: anomaly detection is one of the cornerstones of Industry 4.0. This machine learning technology, used in particular in predictive maintenance, is based on a device’s healthy data to build a model of normal operation from which any derivation constitutes what is known as an anomaly.

Anomaly detection is a far-reaching technological advance, with the power to lift the brakes faced by many manufacturing companies in the Industry 4.0 era.

But can such a sophisticated tool be easily adopted by business experts ? Do all maintenance managers now need to have Data Science knowledge to take part in this new era of the industry ?

Good news : the answer is no.

Thanks to DiagFit, the predictive maintenance software developed by Amiral Technologies in partnership with the CNRS, you don’t need to be an expert in data analysis to use a predictive maintenance solution based on anomalies detection.

Anomalies detection enables maintenance teams to :

Anticipate possible breakdowns and malfunctions …

…that could occur on their equipment and hinder the operational continuity of the production line.

Plan machine stoppages

Once anomalies have been detected, plan interventions on their equipment at the right time
–> Save time.

Increased productivity

In Industry 4.0, where high demands are placed on productivity, safety and availability, the use of predictive maintenance is indispensable.

But for many companies in the industrial sector, implementing such a solution comes up against a number of obstacles : lack of training for teams, time required to build predictive models, diversity of equipment to be analyzed, lack of knowledge to decipher sensor data… And what if all these obstacles were no longer relevant ?

DiagFit : fast, easy implementation of anomalies detection

The result of years of research in the laboratories of the prestigious CNRS, DiagFit blind-mode failure prediction software makes anomaly detection accessible to all production sites. It differs from conventional solutions in several ways :

No code

Surface usable by all trained business experts, without need for programming or data science skills.

Fast model production

This “BUILD” stage lasts only a few hours or days, compared with weeks or even months for conventional predictive maintenance software. The model generated can then be associated with a piece of equipment for the operational phase, known as “RUN”.

Blind mode

The lack of historical failure data has long been one of the main obstacles to the democratization of predictive maintenance. With blind failure prediction, the algorithm uses the device's healthy data to build a model of normal operation and detect any departure from this model, which is then qualified as an anomaly.

Agnostic

It doesn't need to know the type of equipment it is monitoring, and can therefore be easily implemented in all production sites, whatever the devices to be controlled and the type of data they produce.

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