The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing.,The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN.,The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models.,The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference.,The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/

中文翻译：

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基于堆叠去噪自编码器和卷积神经网络的滚动轴承智能故障诊断方法

本研究的目的是分析滚动轴承的智能故障诊断方法,滚动轴承的振动信号数据时间序列长,噪声干扰强,给轴承故障的准确诊断带来很大困难。针对这些问题，本文提出了一种基于堆叠去噪自编码器（SDAE）和卷积神经网络（CNN）的智能故障诊断模型。SDAE 用于处理多维度和噪声干扰的时间序列数据。然后将降维后的样本放入CNN模型中,通过CNN中隐藏层的卷积和池化操作得到故障分类结果,通过实验验证和对比实验分析验证了所提方法的有效性。结果表明，本文提出的模型在三个噪声级别下平均分类准确率可达96.5%，比传统模型和单一深度学习模型高3-13%。,本文提出的SDAE-CNN组合模型可以对原始振动信号数据进行去噪和降维，提取滚动轴承图像样本中的深度特征。因此，该模型对于时间序列较长、噪声干扰较大的滚动轴承振动信号数据具有更准确的故障诊断结果。本文的同行评审历史可在：https://publons.com/publon/10.1108/ ILT-11-2019-0496/ 比传统模型和单一深度学习模型高3-13%。,本文提出的SDAE-CNN组合模型可以对原始振动信号数据进行去噪和降维，提取图像样本中的深度特征滚动轴承。因此，该模型对于时间序列较长、噪声干扰较大的滚动轴承振动信号数据具有更准确的故障诊断结果。本文的同行评审历史可在：https://publons.com/publon/10.1108/ ILT-11-2019-0496/ 比传统模型和单一深度学习模型高3-13%。,本文提出的SDAE-CNN组合模型可以对原始振动信号数据进行去噪和降维，提取图像样本中的深度特征滚动轴承。因此，该模型对于时间序列较长、噪声干扰较大的滚动轴承振动信号数据具有更准确的故障诊断结果。本文的同行评审历史可在：https://publons.com/publon/10.1108/ ILT-11-2019-0496/