Comparison of MLP and RBF neural networks for bearing remaining useful life prediction based on acoustic emission

In this research, the efficiency of multilayer perceptron (MLP) and radial basis function (RBF) neural networks in estimating the remaining useful life (RUL) of angular contact ball bearing based on acoustic emission signals are investigated. To capture the bearing acoustic emission signals, an appropriate laboratory setup is used. Acoustic emission signal processing is carried out in the time and frequency domain and 102 different features are extracted. Prognostic feature selection have been used to reduce the dimension of the extracted features. Applications of the different training algorithms in MLP neural network are compared for bearing RUL prediction. The results indicate that acoustic emission is a good method for bearing RUL prediction. Mobility, Square-mean-root, and Count are the best time domain features based on the used feature selection method. Also, the Frequency center, Signal power, and F60 are the best frequency domain features. It was shown that between different backpropagation training algorithms for MLP neural net, Levenberg Marquardt has the lowest SSE error of 7.86 for the prediction of bearing remaining useful life based on frequency domain features. Moreover, comparison of RBF and MLP neural networks shows that RBF neural networks presents the best performance with SSE error of 2.85.