Rolling bearing fault diagnostics using artificial neural networks based on Laplace wavelet analysis

Abstract

A study is presented to explore the performance of bearing fault diagnosis using three types of artificial neural networks (ANNs), namely, Multilayer Perceptron (MLP) with BP algorithm, Radial Basis Function (RBF) network, and Probabilistic Neural Network (PNN). The time domain vibration signals of a rotating machine with normal and defective bearings are preprocessed using Lapalce wavelet analysis technique for feature extraction. The extracted features are used as inputs to all three ANN classifiers: MLP, RBF, and PNN for four-class: Healthy, outer, inner and roller faults identification. The procedure is illustrated using the experimental vibration data of a rotating machine with different bearing faults. The results show the relative effectiveness of three classifiers in detection of the bearing condition with different learning speeds and success rates.

International Journal of Engineering, Science and Technology, Vol. 2, No. 6, 2010, pp. 278-290