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Developing an innovative platform for predictive maintenance. A solution capable of:

Preventing imminent failures from analysing the readings of sensors mounted on production systems

Enabling intervention strategies to avoid downtime often associated with damaging consequences on the production cycle and the very integrity of components, thus avoiding inevitable negative economic consequences

Solution found

Platform integrating components for monitoring, anomaly detection and recovery, fault and failure prediction and interpretation, and maintenance support.

Advanced technological solution

Use of machine learning algorithms (data-driven approach) for time series analysis based on various techniques including Gradient Boosting and LSTM-Autoencoder, for detecting correlation between measurements and determining anomalous trends in the data.
Two verticalisations:

Predictive maintenance of physical production systems

Predictive maintenance of production information systems

Explanation, augmented and virtual reality for modelling the maintenance process to ensure support for the maintainer (model-based approach).

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