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Comparison of MLP and RBF Neural Networks for Bearing Remaining Useful Life Prediction Based on Acoustic Emission

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART J-JOURNAL OF ENGINEERING TRIBOLOGY(2023)

Shahid Beheshti Univ

Cited 10|Views7
Abstract
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.
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Key words
MLP neural networks,RBF neural networks,remaining useful life,angular contact bearing,acoustic emission
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要点】:本研究比较了多层感知器(MLP)和径向基函数(RBF)神经网络在利用声发射信号预测角接触球轴承剩余使用寿命(RUL)方面的性能,发现RBF神经网络具有更优表现。

方法】:通过实验室设置捕获轴承的声发射信号,并在时域和频域进行信号处理,提取102个特征,使用预后特征选择方法降低特征维度。

实验】:实验采用适当实验室设置收集数据,使用声发射信号,经过处理后,利用MLP和RBF神经网络进行RUL预测,结果显示基于频域特征的Levenberg Marquardt算法在MLP网络中表现最佳,SSE误差为7.86,而RBF网络整体表现更佳,SSE误差为2.85。数据集名称未提及。