Classification of gear faults using cumulants and the radial basis function network

Every tooth in a gearbox is alternately meshing and detaching during its operation. Hence, the loading condition of the tooth is alternately changing. Such a condition will make the tooth easily subject to spalling and worn. Moreover, Gaussian type of noise which is always embedded in the measurements makes the signal-to-noise ratio (SNR) of the collected data low and difficult to extract in fault-related features. This paper aims to propose an approach for gear fault classification by using cumulants and the radial basis function (BRF) network. The use of cumulants can minimize Gaussian noise and increase the SNR. The RBF network has proven to be superior to back-propagation networks. The RBF network provides better functions to approximate non-linear inputs and faster in convergence. In this paper, experiments have been conducted on a real gearbox. The cumulants calculated from the vibration signal collected from the inspected gearbox are used as input features. The RBF network is then used as a classifier for various kinds of operating conditions of the gearbox. Results show that the method of classification by combining cumulants and the RBF network is promising and achieved better accuracy.