一种基于CNN的滚动轴承退化指标构建方法

针对传统滚动轴承退化指标构建方法高度依赖于人工筛选特征的问题,提出基于卷积神经网络的端到端的滚动轴承性能退化指标构建方法。该方法在Softmax的输出端设置两个节点,分别代表正常和失效状态,以正常状态归一化的幅值谱为训练样本,以待评估数据在正常节点输出概率为基础,构建了滚动轴承性能退化指标。通过在不同实验数据集中的应用,以及与其他指标的对比,验证了该方法的有效性和优越性。

A Rolling Bearing Degradation Index Construction Method Based on CNN

In order to figure out the matter that the traditional construction manners of rolling bearing degradation index extremely rely on manual selection features,an end-to-end construction method of rolling bearing performance degradation indicator based on convolutional neural networks was put forward.In this method,two nodes were set at the output end of Softmax,respectively representing the normal state and the failure state.Normalized vibration amplitude spectrum of the healthy state was taken as the training sample,and the rolling bearing performance degradation index was constructed based on the probability of normal state.The effectiveness and superiority of this method were verified by application in different experiment data sets and comparison with other indices.