Airbus Helicopters

Eurocopter

Hervé Morel

Nicolas Queiras

Jonathan Rodriguez

Hugo Andre

Jérémy Jouve

Mohamed El Badaoui

Anas Had

Joël Mouterde

Stéphane Cerqueira

A method and a device for detecting a fault in a mechanical system comprising a rotating member and a vibration sensor transmitting a temporal vibration signal. The method comprises processing the temporal vibration signals transmitted over a predetermined period in order to transform the temporal vibration signals firstly into angular vibration signals, then into frequency vibration signals. Next, series of amplitudes are generated, each series being associated with a specific frequency channel and comprising the amplitudes of the frequency vibration signals for this frequency channel. The frequency channels displaying an increasing trend consistent with the fault of interest are then identified. The frequency channels that display an increasing trend are then compared with characteristic frequencies of a known fault of interest in order to check for the possible presence of such a fault in the mechanical system.

Method and device for detecting a fault in a mechanical system comprising at least one rotating member

The invention relates to a method of detecting and identifying a defect or an adjustment error of a rotorcraft rotor using an artificial neural network (ANN), the rotor having a plurality of blades and a plurality of adjustment members associated with each blade; the network (ANN) is a supervised competitive learning network (SSON, SCLN, SSOM) having an input to which vibration spectral data measured on the rotorcraft is applied, the network outputting data representative of which rotor blade presents a defect or an adjustment error or data representative of no defect, and where appropriate data representative of the type of defect that has been detected.

Method and a system for detecting and locating an adjustment error or a defect of a rotorcraft rotor

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Lamanon, France

Hervé Morel