采用多尺度自适应选择卷积神经网络的轴承故障诊断研究

张玺君; 尚继洋

doi:10.7652/xjtuxb202402013

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采用多尺度自适应选择卷积神经网络的轴承故障诊断研究

更新时间：2025-05-13

- 采用多尺度自适应选择卷积神经网络的轴承故障诊断研究
- Bearing Fault Diagnosis Based on Multi-Scale Adaptive Selective Convolutional Neural Network
- 西安交通大学学报 2024年58卷第2期页码：127-135
- 作者机构：
  
  兰州理工大学计算机与通信学院,兰州,730050
- 作者简介：
  
  : 2023-07-09。作者简介: 张玺君(1980—),男,副教授,硕士生导师。基金项目: 国家自然科学基金资助项目(61966023)
- 基金信息：
  
  : 2023-07-09。作者简介: 张玺君(1980—),男,副教授,硕士生导师。基金项目: 国家自然科学基金资助项目(61966023);甘肃省高等学校创新基金资助项目(2021A-028);甘肃省科技计划-自然科学基金重点资助项目(22JR5RA226)。
- DOI：10.7652/xjtuxb202402013
  中图分类号： TP277
- 网络首发：2024-02-10，
  
  纸质出版：2024
- 稿件说明：
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张玺君, 尚继洋. 采用多尺度自适应选择卷积神经网络的轴承故障诊断研究[J]. 西安交通大学学报, 2024,58(2):127-135.

ZHANG Xijun, SHANG Jiyang. Bearing Fault Diagnosis Based on Multi-Scale Adaptive Selective Convolutional Neural Network[J]. 2024, 58(2): 127-135.
张玺君, 尚继洋. 采用多尺度自适应选择卷积神经网络的轴承故障诊断研究[J]. 西安交通大学学报, 2024,58(2):127-135. DOI： 10.7652/xjtuxb202402013.

ZHANG Xijun, SHANG Jiyang. Bearing Fault Diagnosis Based on Multi-Scale Adaptive Selective Convolutional Neural Network[J]. 2024, 58(2): 127-135. DOI： 10.7652/xjtuxb202402013.

摘要

针对轴承故障诊断方法中传统多尺度卷积神经网络对不同尺度的特征只是简单拼接

而未考虑不同尺度的特征差异的问题

提出一种多尺度自适应选择卷积神经网络轴承故障诊断模型(MSASCNN)。通过不同大小的宽卷积筛选原始轴承振动信号中的特征

合并为初始特征; 构建多尺度自适应选择卷积块

提取不同尺度的特征

利用改进的注意力机制自适应调整不同尺度的特征权重

加入残差连接

防止模型退化; 通过分类器完成轴承故障诊断。在凯斯西储大学轴承数据集和XJTU-SY轴承数据集上的实验结果表明:在模型改进实验中

与没有改进注意力机制的模型相比

所提模型的轴承故障诊断准确率提升了1.98%; 在不同信噪比的噪声干扰环境中

所提模型的轴承故障诊断准确率均高于93%。

Abstract

Aiming at the issue that conventional multi-scale convolutional neural networks in bearing fault diagnosis simply spliced features of different scales without considering the feature differences of different scales

a multi-scale adaptive selective convolutional neural network(MSASCNN)bearing fault diagnosis model is proposed. The features of the original bearing vibration signals are selected in the model through wide convolution of different sizes and combined into initial features. Multi-scale adaptive selective convolutional blocks are constructed to extract features of different scales

and the improved attention mechanism is used to adjust the feature weights of different scales adaptively

and residual connections are added to prevent model degradation. The bearing fault diagnosis is completed by the classifier. The experimental results on Case Western Reserve University bearing dataset and XJTU-SY bearing dataset show that in the model improving experiment

the bearing fault diagnosis accuracy of the proposed model increases by 1.98% compared with the model without improved attention mechanism. In the noise interference environment with different signal-to-noise ratios

the bearing fault diagnosis accuracy of the proposed model is higher than 93%.

关键词

Keywords

references

LIU Ruonan, YANG Boyuan, ZIO E, et al. Artificial intelligence for fault diagnosis of rotating machinery: a review [J]. Mechanical Systems and Signal Processing, 2018, 108: 33-47.

RAI A, UPADHYAY S H. A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings [J]. Tribology International, 2016, 96: 289-306.

GAO Zhiwei, CECATI C, DING S X. A survey of fault diagnosis and fault-tolerant techniques: part Ⅱ fault diagnosis with knowledge-based and hybrid/active approaches [J]. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3768-3774.

LEI Yaguo, YANG Bin, JIANG Xinwei, et al. Applications of machine learning to machine fault diagnosis: a review and roadmap [J]. Mechanical Systems and Signal Processing, 2020, 138: 106587.

JIAO Jinyang, ZHAO Ming, LIN Jing, et al. A comprehensive review on convolutional neural network in machine fault diagnosis [J]. Neurocomputing, 2020, 417: 36-63.

ZHANG Wei, PENG Gaoliang, LI Chuanhao, et al. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals [J]. Sensors, 2017, 17(2): 425.

LI Xiaohu, WAN Shaoke, LIU Shijie, et al. Bearing fault diagnosis method based on attention mechanism and multilayer fusion network [J]. ISA Transactions, 2022, 128(Part B): 550-564.

ZHANG Shuo, LIU Zhiwen, CHEN Yunping, et al. Selective kernel convolution deep residual network based on channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis [J]. ISA Transactions, 2023, 133: 369-383.

RUAN Diwang, WANG Jin, YAN Jianping, et al. CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis [J]. Advanced Engineering Informatics, 2023, 55: 101877.

LIANG Pengfei, WANG Wenhui, YUAN Xiaoming, et al. Intelligent fault diagnosis of rolling bearing based on wavelet transform and improved ResNet under noisy labels and environment [J]. Engineering Applications of Artificial Intelligence, 2022, 115: 105269.

LIANG Haopeng, CAO Jie, ZHAO Xiaoqiang. Multi-scale dynamic adaptive residual network for fault diagnosis [J]. Measurement, 2022, 188: 110397.

YAN Shen, SHAO Haidong, XIAO Yiming, et al. Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises [J]. Robotics and Computer-Integrated Manufacturing, 2023, 79: 102441.

HUANG Wenyi, CHENG Junsheng, YANG Yu, et al. An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis [J]. Neurocomputing, 2019, 359: 77-92.

QIAO Huihui, WANG Taiyong, WANG Peng, et al. An adaptive weighted multiscale convolutional neural network for rotating machinery fault diagnosis under variable operating conditions [J]. IEEE Access, 2019, 7: 118954-118964.

CHEN Xiaohan, ZHANG Beike, GAO Dong. Bearing fault diagnosis base on multi-scale CNN and LSTM model [J]. Journal of Intelligent Manufacturing, 2021, 32(4): 971-987.

JIA Linshan, CHOW T W S, WANG Yu, et al. Multiscale residual attention convolutional neural network for bearing fault diagnosis [J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-13.

HUANG Yajing, LIAO Aihua, HU Dingyu, et al. Multi-scale convolutional network with channel attention mechanism for rolling bearing fault diagnosis [J]. Measurement, 2022, 203: 111935.

康涛, 段蓉凯, 杨磊, 等. 融合多注意力机制的卷积神经网络轴承故障诊断方法 [J]. 西安交通大学学报, 2022, 56(12): 68-77.

KANG Tao, DUAN Rongkai, YANG Lei, et al. Bearing fault diagnosis using convolutional neural network based on a multi-attention mechanism [J]. Journal of Xi'an Jiaotong University, 2022, 56(12): 68-77.

SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deeper with convolutions [C]//2015 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Piscataway, NJ, USA: IEEE, 2015: 1-9.

SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4: inception-ResNet and the impact of residual connections on learning [C]//Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. Palo Alto, CA, USA: AAAI Press, 2017: 4278-4284.

LIANG Haopeng, CAO Jie, ZHAO Xiaoqiang. Multibranch and multiscale dynamic convolutional network for small sample fault diagnosis of rotating machinery [J]. IEEE Sensors Journal, 2023, 23(8): 8973-8988.

杨永灿, 谭庆慧, 秦宇翔. 基于注意力机制的多尺度卷积神经网络电机轴承故障诊断 [J]. 云南水力发电, 2023, 39(6): 51-56.

YANG Yongcan, TAN Qinghui, QIN Yuxiang. Multi scale convolutional neural network fault diagnosis of motor bearing based on attention mechanism [J]. Yunnan Water Power, 2023, 39(6): 51-56.

SMITH W A, RANDALL R B. Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study [J]. Mechanical Systems and Signal Processing, 2015, 64-65: 100-131.

LI Xiang, WANG Wenhai, HU Xiaolin, et al. Selective kernel networks [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Piscataway, NJ, USA: IEEE, 2019: 510-519.

WANG Qilong, WU Banggu, ZHU Pengfei, et al. ECA-net: efficient channel attention for deep convolutional neural networks [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Piscataway, NJ, USA: IEEE, 2020: 11531-11539.

XIN Ge, LI Zhe, JIA Limin, et al. Fault diagnosis of wheelset bearings in high-speed trains using logarithmic short-time Fourier transform and modified self-calibrated residual network [J]. IEEE Transactions on Industrial Informatics, 2022, 18(10): 7285-7295.

张龙, 胡燕青, 赵丽娟, 等. 采用递归图编码技术与残差网络的滚动轴承故障诊断 [J]. 西安交通大学学报, 2023, 57(2): 110-120.

ZHANG Long, HU Yanqing, ZHAO Lijuan, et al. Fault diagnosis of rolling bearings using recurrence plot coding technique and residual network [J]. Journal of Xi'an Jiaotong University, 2023, 57(2): 110-120.

Case Western Reserve University Bearing Data Center. Bearing data file [EB/OL].(2022-08-02)[2023-05-01]. https://engineering.case.edu/bearingdatacenter/download-data-file.

HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Piscataway, NJ, USA: IEEE, 2016: 770-778.

WANG Biao, LEI Yaguo, LI Naipeng, et al. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings [J]. IEEE Transactions on Reliability, 2020, 69(1): 401-412.

VAN DER MAATEN L, HINTON G. Visualizing data using t-SNE [J]. Journal of Machine Learning Research, 2008, 9(86): 2579-2605.

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