堆叠自编码网络性能优化及其在滚动轴承故障诊断中的应用

张西宁; 向宙; 夏心锐; 李立帆

doi:10.7652/xjtuxb201810007

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堆叠自编码网络性能优化及其在滚动轴承故障诊断中的应用

更新时间：2025-07-09

- 堆叠自编码网络性能优化及其在滚动轴承故障诊断中的应用
- Optimization of Stacking Auto-Encoder with Applications in Bearing Fault Diagnosis
- 西安交通大学学报 2018年52卷第10期页码：49-56+87
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2018-04-19。作者简介: 张西宁(1965-),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(51275379);国家自然科学基金创新研究群体资助项目(51421004)
- DOI：10.7652/xjtuxb201810007
  中图分类号： TH17
- 网络首发：2018-10-10，
  
  纸质出版：2018
- 稿件说明：
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张西宁, 向宙, 夏心锐, 等. 堆叠自编码网络性能优化及其在滚动轴承故障诊断中的应用[J]. 西安交通大学学报, 2018,52(10):49-56+87.

Optimization of Stacking Auto-Encoder with Applications in Bearing Fault Diagnosis[J]. 2018, 52(10): 49-56+87.
张西宁, 向宙, 夏心锐, 等. 堆叠自编码网络性能优化及其在滚动轴承故障诊断中的应用[J]. 西安交通大学学报, 2018,52(10):49-56+87. DOI： 10.7652/xjtuxb201810007.

Optimization of Stacking Auto-Encoder with Applications in Bearing Fault Diagnosis[J]. 2018, 52(10): 49-56+87. DOI： 10.7652/xjtuxb201810007.

摘要

为了解决堆叠自编码网络在参数较多时的梯度弥散问题

对网络每层的编码值进行了统计分析

发现大部分分布于激活函数的饱和区

这直接导致了神经元权值梯度的消失。为此

引入了一种标准化策略

将神经元按照样本进行归一化

然后引入两个待学习参数进行缩放和平移

最后通过激活函数输出到下一级神经元。运用带标准化的堆叠自编码网络进行滚动轴承故障诊断

将振动信号的频谱输入到网络中。与普通堆叠自编码网络相比

该标准化策略可有效地使网络编码值均匀分布

如将第一层编码值的熵从0.88 bit提高到了16.29 bit。带标准化的堆叠自编码网络可有效提高网络的抗噪能力和训练速度:在凯斯西储大学滚动轴承数据集上

当人为添加噪声信号的信噪比为0 dB时

识别正确率从16.18%提高到了100%; 在实验室实测数据集上

不仅训练时间下降了37.22%

而且识别正确率从97.93%提高到了99.95%。对网络的编码值进行分析以及引入的标准化策略

可为科研技术人员构建堆叠自编码网络时提供参考

也为滚动轴承故障诊断提供了一种策略。

Abstract

To solve the problem of gradient dispersion in stacking auto-encoder(SAE)with large number of parameters

we analyze the distribution of encoding values in each hidden-layer of network. It is found that most of them are distributed in the saturation area of the activation function

which directly leads to a weight gradient loss

thus a normalizing strategy is introduced. The node is normalized according to the sample

then two parameters are introduced to scale and move the encoding values. Then the modified values are passed to the activation function to next layer. The fault diagnosis of rolling bearing is carried out by the normalized SAE

and the spectrum of vibration signal is input into the network. Compared with ordinary SAE

the encoding values of normalized SAE are more well-distributed. For example

the entropy of encoding values in the first level is increased from 0.88 bit to 16.29 bit. The normalized SAE has higher anti-noise ability and faster training rate. When the signal to noise ratio(SNR)is 0 dB

the recognition accuracy is increased from 16.18% to 100% on the rolling bearing data sets of Case Western Reserve University. On laboratory data sets

the training time is decreased by 37.22%

and the recognition accuracy is increased from 97.93% to 99.95%. The introduced normalizing strategy provides a reference for subsequent research on the construction of SAE

and also provides a strategy for fault diagnosis of rolling bearings.

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