1. 三峡大学水电机械设备设计与维护湖北省重点试验室
2. 武汉科技大学冶金装备及其控制教育部重点试验室
纸质出版:2021
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陈保家, 陈学力, 沈保明, 等. CNN-LSTM深度神经网络在滚动轴承故障诊断中的应用[J]. 西安交通大学学报, 2021,(6):28-36.
针对大数据下的滚动轴承振动信号自适应故障特征提取与智能诊断问题
提出了一种结合卷积神经网络(CNN)与长短时记忆网络(LSTM)的故障诊断模型。首先通过网格搜索算法寻找到当前模型的最优初始参数;然后以原始一维振动信号作为模型的输入
利用网络CNN层自适应提取短时特征信并降维后作为LSTM层输入;接着利用LSTM层学习特征信息并训练神经网络模型;最后
网络输出层利用Softmax函数实现多故障模式识别
完成故障诊断。使用Spectra Quest机械故障综合模拟试验台实测数据集对模型进行验证
试验结果表明
与多层感知器、LSTM网络以及经典的LeNet5、AlexNet、VGG相比
所提出的CNN-LSTM模型的分类平均准确率可达99%以上
且模型结构比其他模型更简单
训练时间更短;同时
通过K折叠交叉验证算法对模型进行评价
结果表明CNN-LSTM模型计算误差较小且网络训练充分
未出现过拟合或欠拟合情况。
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