基于LSTM-RNN的滚动轴承故障多标签分类方法

为实时监测砂带磨损状态，采用基于磨削声信号与电流信号的监测方案。首先，利用时域分析方法与小波包分析方法提取砂带磨损信号特征，通过朴素贝叶斯方法融合两种信号，从而识别砂带磨损状态；其次，为提高砂带磨损状态识别准确率，针对朴素贝叶斯方法的分类特性，改进了一种基于Fisher判别率与互信息的信号特征选择方法。实验结果表明，利用基于Fisher判别率与互信息方法能够挑选出可分性好同时特征间相关性弱的信号特征，基于朴素贝叶斯的砂带磨损状态识别方法能够准确地识别砂带磨损状态。

A Multi-label Fault Classification Method for Rolling Bearing Based on LSTM-RNN

The wear of abrasive belt is monitored based on the grinding sound and current. First, the feature of these signalsare extracted by the time domain analysis method and the wavelet packet analysis method, and merged by Naive Bayes classifier to identify the abrasive belt. Furthermore, to increase the accuracy of identification, the Naive Bayes classifieris improved by introducing a signal feature selection method based on Fisher discriminant rate and mutual information. The experimental results show that the signal features with good separability and weak correlation can be selected by the improved feature selection method, and the wear state recognition method based on Naive Bayes classifier can identify the abrasive wear state accurately.