Gears and bearings are more and more used in every industrial area mainly due to their strong reliability. Nevertheless as every mechanical transmission system, failures appear during time life. It induces critical damage, time cost for maintenance services to repair the fault potentially on duty. A wide part of work in the scientific community already provides a large quantity of features to follow health status of these systems (e.g., RMS, kurtosis, crest factor, FM0) in order to detect the fault as soon as possible.

Since few years, methods developed in signal post-processing are coupled with Machine Learning (ML). ML allows ability to detect novelty or fault based on a trained algorithm. According to the literature [1], to identify the type of damage, a supervised algorithm is needed. Consequently an accurate diagnosis implies labelled data which are often difficult to obtain practically.

The aim of this paper is to provide keys, based on our knowledge about features in Structural Health Monitoring (SHM), to get higher information level in classification by adding a qualitative analysis (type of damage) without label or information about the type of fault.

Work carries on a measurement database. The assumption is made about two classes “healthy” / “faulty” using a supervised algorithm. The contribution of our work brings a new step in the default analysis by adding a probability for a defect case to be identified. Indeed, by combining some sensitive features selected for their relevance to describe a type of fault, a probability to have this particular default can be given. This classification is tested against three fault classes: bearing, gear generalized, gear localized.

Results show that a probability for having bearing fault can be identified using this method contrary to the gear generalized and localized fault which are more complex to characterize. This new step enables to help maintenance services to focus more efficiently on the incriminated faulty part of the system, inducing a reduction of time to repair for maintenance services, a shorter out of order time leading to a significant productivity gain.