Acoem France

Inria Institut National De Recherche En Informatique Et En Automatique

Toyota Motor Europe Nv/sa

Kamel Mekhnacha

Katsuhiro Sakai

Gabriel Othmezouri

Hiromichi Yanagihara

Christian Laugier

Bertrand Wascat

Thierry Mazoyer

Patrick Labeyrie

Christopher Tay Meng Keat

Guillame Lavaure

Emmanuel Mazer

Automatic fault diagnosis is performed on vibration data sensed from a machine. A set of faults to screen for is identified from the machine configuration. For each fault there are characteristic symptoms. For each characteristic symptom, there is a corresponding indication used to diagnose the symptom. The indications are based on analyses of the current vibration data. The diagnosed symptoms have weights assigned according to a Bayesian network, and are used to derive a Bayesian probability for the fault. A fault having a Bayesian probability exceeding a threshold value is identified as being present in the machine. For each fault a confidence level is derived. The confidence level for a first fault is based on a similarity between characteristic symptoms for the first fault and characteristic symptoms for each one of the other faults being screened.

Automatic rotating-machine fault diagnosis with confidence level indication

Growing community of inventors

Grenoble, France

Kamel Mekhnacha