结合声发射和卷积神经网络的轴承RUL预测方法

Bearing Remaining Life Prediction Method Coupling with Acoustic Emission and Convolutional Neural Network

根据声发射信号具有对早期损伤敏感性高和频带宽等特点,提出一种结合声发射信号和卷积神经网络的方法,实现滚动轴承的RUL预测。该轴承RUL预测方法主要包括:对原始信号的分频段滤波和特征值提取得到高维特征集;将高维特征集组合成二维神经元作为卷积神经网络的输入,并构建和训练网络以达到预测剩余寿命的目的。通过从实验中得到的数据验证了该预测方法的可行性,并且具有较高的准确性。结合使用卷积神经网络后不但解决了特征值数量大和如何合理利用高维特征问题,而且还得到了较好的RUL预测效果。

According to the characteristics of acoustic emission signal with high sensitivity to early damage and frequency bandwidth,a method coupling with acoustic emission signal and convolutional neural network was proposed to realize the RUL prediction of rolling bearings.The bearing RUL prediction method mainly includes:sub-band filtering and feature value extraction of the original signal to obtain a high-dimensional feature set;combining with the high-dimensional feature set into a two-dimensional neuron as the input of a convolutional neural network,and constructing and training the network to achieve the prediction of remaining life.The feasibility of the prediction method is verified by using the experimental,and it has high accuracy.The coupling use of convolutional neural networks not only solves the problem of large number of eigenvalues and how to reasonably use high-dimensional features,but also obtains the better RUL prediction results.