The blind failure prediction software

DiagFit

 

DiagFit is our predictive maintenance software that enables manufacturers to anticipate and plan their machine shutdowns. The software is based on state-of-the-art machine learning algorithms for industrial time series management.

DiagFit

 

 

These properties are extremely rich and allow the efficient detection of weak signals of failure, ageing or approaching end of life of equipment.

The specification generation, an invention at the heart of the software, allows the construction of mathematical properties of time series from industrial equipment or IIoT sensors.

Predictive maintenance: 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. This allows the software to be implemented extremely quickly as the industry is equipped with increasingly reliable equipment that rarely fail.

Thanks to its unsupervised approach, the software learns from the sound data of the equipment and automatically builds a robust and reliable normality space. This operation allows the detection of anomalies with high accuracy and a low false alarm rate. It is the management of discriminating properties from our specification generators that makes detection so effective

 

Read article

The advantages of DiagFit

The advantages of DiagFit

Request a demo

Blind mode

Works without historical failure data

No-code

No data science skills required

Agnostic

Sensor and equipment agnostic

Quick

Models built in hours/days instead of weeks/months

Accurate

Reduced false alarm rate

Software based on the knowledge of domain experts

DiagFit is incredibly fast and easy to use as it has been 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.

Once generated, the predictive model can then be used and linked to operational equipment in the “RUN” step. Domain experts are then alerted when deviations from the normality space occur and benefit from the indications given by the software to identify their origin. These deviations, called anomalies, can be accepted or rejected by maintenance operators who have functional knowledge of the equipment concerned.

This enrichment by experts allows the model to learn over the life of the equipment and to be even more efficient.

 

*can be carried out by our experts if desired, please contact us for more information.

 

 

Download the product sheet 

BUILD: model validation
BUILD: model validation

RUN : acceptance or rejection of anomalies
RUN: acceptance or rejection of anomalies

Labelling of anomalies

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

The software will then be able to recognise its signature automatically in the future and give the user a confidence score for its diagnosis.

As with all alerts, the expert can then confirm or deny the prediction.

 

Labelling
Labelling

 

Download the product sheet 

RUN: anomaly being detected and recognised by DiagFit
RUN: anomaly being detected and recognised by DiagFit

RUN: anomalies awaiting acceptance and labelling
RUN: anomalies awaiting acceptance and labelling

Deployment

  • Public cloud
  • Private cloud 
  • On-premise 
  • Integrated with a third party platform 
  • Embedded (please contact us)

Architecture and interfaces

  • Web microservices architecture
  • On linux server with Dockers
  • Minimum configuration: 4 core processors at 3 GHz
  • REST API with interactive online documentation
  • Alert notification via MQTT protocol

 

Clients

Clients

Clients Clients Clients Clients Clients Clients Clients Clients Clients

Use Cases

Use Cases

Failure prediction on an anonymised ship system

Using anonymised health data acquired over a year on the ship, DiagFit was used to create a unique model within hours that captures correlations between sensor data and produces a health status for each sensor

Quality prediction and normative maintenance for plastic presses

Using the data from the variable speed drive on the press and the quality measurements at the output of the press, DiagFit analyses the deviation of the specifications used to derive a virtual quality sensor.

Pipe crack detection

Based on the eddy current data used to monitor the surface of pipelines, DiagFit can classify healthy and unhealthy pipe sections

See all use cases