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Predictive maintenance and anomaly detection

Skiply requested a study to develop predictive maintenance and anomaly detection solutions.


Type of service

Artificial Intelligence Study , Artificial Intelligence Innovation

Duration of the project

6 months

The company

Skiply, a Savoy-based company, offers solutions for customer experience improvement, facility management and process automation thanks to its smart buttons and satisfaction terminals.

of the project

Skiply’s project is made of two main points:

  • Carry out a study of predictive maintenance solutions to anticipate the failure of batteries in smart boxes.
  • Study the development of an automatic anomaly detection system to measure customer satisfaction.
image_skiply borne-satisfaction-signature-carré

of the project

Type of service

Study service

Development of the project

This 6-month project combines the intervention of a USMB LISTIC researcher, an IDEFICS engineer and the provision of computer resources within the MUST digital platform.

Results and perspectives

The first phase of the project, which concerned predictive maintenance, began with a transfer of skills, followed by a study on data structuring and finally the implementation of a first AI solution allowing the classification of batteries according to their state.
The second phase of the project, which is still under study, is intended firstly to detect battery anomalies and then to predict failures, which will make it possible to initiate predictive maintenance.
This second project, which has not yet been developed, would involve an IDEFICS engineer, a researcher and an engineering school trainee.

IDEFICS engineer
Icone Lisitic
LISTIC researcher
Logo Skiply2
Skiply Supervisor
MUST Platform’s team
In support