Application of Order Cepstrum and Neural Network to Gear Fault Detection

A study is presented to apply order cepstrum and radial basis function (RBF) artificial neural network (ANN) for gear fault detection during speed-up process. This method combines computed order tracking, cepstrum analysis with ANN. Firstly, the vibration signal during speed-up process of the gearbox is sampled at constant time increments and then is resampled at constant angle increments. Secondly, the resampled signals are processed by cepstrum analysis. The order cepetrum of with normal, wear and crack fault are processed for feature extracting. In the end, the extracted features are used as inputs to RBF for recognition. The RBF is trained with a subset of the experimental data for known machine conditions. The ANN is tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results show the effectiveness of order cepstrum and RBF in detection of the gear condition.