State-parameter estimation for a helical gear transmission with pitting defects

Abstract

During the operation cycle of geared transmissions, remaining useful lifetime predictions are key information for maintenance and future design decisions. These predictions are usually obtained by combining modelling efforts with limited sensor data. This contribution proposes an estimation method that aims to be robust to the operating conditions and the statistical properties of the introduced damage. As damage on the tooth surface causes a reduction in the gear pair mesh stiffness due to the changed contact conditions, this time-varying mesh stiffness is proposed as health indicator. The mesh stiffness is parameterized in a piecewise interpolation scheme to guarantee the local detectability of the involved parameters. The parameters are estimated concurrently with the states using an augmented extended Kalman filter. The method is applied on a single helical gear pair of an industrial gearbox, combining a lumpedparameter contact model with angular position data of both gear shafts. For the validation, virtual measurement data are generated using a gear contact model with pitting defects. The estimation results show a proof of concept and highlight the potential of the method for more complex cases.