After having carried out a one-year experiment of its HUMS MOKA on a fleet of armoured vehicles of the French Army in co-contracting with ARQUUS, LGM continues its development in predictive maintenance by integrating the DiagFit solution of Amiral Technologies in its AI algorithm platform.

The DiagFit solution from Amiral Technologies (a CNRS spin-off) uses high-performance predictive AI models, capable of learning with little or no failure history.

This technology thus greatly facilitates the deployment of IoTs for critical industrial sectors with high added value. The focus is on the algorithm for automatic feature generation for any kind of time series.

These are therefore high-performance algorithms that will enable our HUMS MOKA to be deployed more quickly in order to provide relevant warnings to system operators. Moreover, the generality of the approach makes it possible to apply them to a very wide range of applications or contexts.

The HUMSs will thus be able to quickly demonstrate a real industrial and operational ROI, by making it possible to characterise the life profiles of the systems with precision, and by highlighting drifts and operating anomalies for the operators.

We also look forward to seeing you at the next Lambda Mu 22 conference in October, where LGM will present the results of a detailed prospective study of the impact of BigData on maintenance, and therefore in particular of AI on reliability studies, conducted for IMdR in collaboration with QUANTMETRY.