FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM

DU HaoFei; ZHANG Chao; LI JianJun

doi:10.16579/j.issn.1001.9669.2023.06.001

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FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM

更新时间：2024-06-07

- FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM
- Journal of Mechanical Strength Vol. 45, Issue 6, Pages: 1271-1279(2023)
- 作者机构：
  
  1. 内蒙古科技大学信息工程学院
  2. 内蒙古科技大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2023.06.001
  CLC： TH17;TP183
- Published：15 December 2023，
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DU HaoFei, ZHANG Chao, LI JianJun. FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM. [J]. Journal of Mechanical Strength 45(6):1271-1279(2023)
DOI：

DU HaoFei, ZHANG Chao, LI JianJun. FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM. [J]. Journal of Mechanical Strength 45(6):1271-1279(2023) DOI： 10.16579/j.issn.1001.9669.2023.06.001.

摘要

针对风机滚动轴承故障诊断需要提取大量复杂特征，提出一种基于注意力机制、ResNext网络和长短时记忆(Long Short Term Memory

LSTM)网络的并行轴承故障诊断模型。首先，将采集的一维振动信号进行预处理；然后，分两路输入到模型中提取特征，其中一路输入到嵌入注意力机制的ResNext模块中，注意力机制可以增加重要特征的权重，减少模型运算量，另一路输入到LSTM网络中提取振动信号在时间序列上的依赖关系；最后，将两路提取到的特征进行融合输入到Softmax层进行故障分类。实验结果表明，与目前基于深度学习的轴承故障诊断方法相比，所提方法在轴承故障分类准确率上表现更好。

Abstract

A large number of complex features need to be extracted for the fault diagnosis of wind turbine rolling bearings. A parallel bearing fault diagnosis model based on attention mechanism

ResNext network and long short-term memory (LSTM) network was proposed. Firstly

the collected one-dimensional vibration signal was preprocessed; then it was input into the model in two ways to extract features

and one of them was input into the ResNext module embedded in the attention mechanism. The attention mechanism can increase the weight of important features and reduce model operations. The other channel was input to the LSTM network to extract the dependence of the vibration signal on the time series. Finally

the two extracted features are fused and input to the Softmax layer for fault classification. The experimental results show that

compared with the current bearing fault diagnosis method based on deep learning

the proposed method performs better in bearing fault classification accuracy.

关键词

故障诊断注意力机制ResNextLSTM

Keywords

Fault diagnosisAttention mechanismResNextLSTM

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