EN基于自适应谱图小波卷积神经网络的风力机轴承故障诊断
基于自适应谱图小波卷积神经网络的风力机轴承故障诊断
- 太阳能学报 2025年46卷第12期 页码：564-573
- 作者机构：
- 作者简介：
- 基金信息：国家自然科学基金项目（52307238）;河北省燕赵青年科学家项目（E2024202109）;天津市重点项目（22JCZDJC00620）
DOI：doi:10.19912/j.0254-0096.tynxb.2024-1462
中图分类号： TH133-
网络出版：2026-01-07，
纸质出版：2025
- 稿件说明：
移动端阅览
徐志成, 罗硕, 张闯, 等. 基于自适应谱图小波卷积神经网络的风力机轴承故障诊断[J]. 太阳能学报, 2025,46(12):564-573.
徐志成, 罗硕, 张闯, et al. 基于自适应谱图小波卷积神经网络的风力机轴承故障诊断[J]. 2025, 46(12): 564-573.
徐志成, 罗硕, 张闯, 等. 基于自适应谱图小波卷积神经网络的风力机轴承故障诊断[J]. 太阳能学报, 2025,46(12):564-573. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1462.
徐志成, 罗硕, 张闯, et al. 基于自适应谱图小波卷积神经网络的风力机轴承故障诊断[J]. 2025, 46(12): 564-573. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1462.
摘要
针对传统智能故障诊断方法在强噪声和复杂工况环境下难以提取可靠特征、诊断准确率低及特征可解释性差的问题
该文提出一种面向航空发电机、风力发电机等大型旋转设备内部轴承的自适应谱图小波卷积神经网络故障诊断方法。该方法考虑多传感器间的交互作用
振动信号转换成图结构数据
将融合猎豹优化算法的自适应谱图小波与重加权小波系数策略嵌入图卷积层
构建自适应谱图小波卷积神经网络。自适应谱图小波卷积神经网络根据振动信号特性动态调整谱图小波的设计参数和分解层数
实现多尺度特征的高效提取
并将不同尺度特征赋予不同权重
增强信号去噪和弱特征提取能力。该方法实现在高噪声环境下端到端的轴承原位故障诊断
同时为图卷积操作提供高效且可解释的框架。实验结果表明
所提方法在高噪声和复杂工况下均表现出良好的诊断性能及鲁棒性。
Abstract
Traditional intelligent fault diagnosis methods face challenges in extracting reliable features
achieving high diagnostic accuracy
and providing feature interpretability under strong noise and complex operating conditions. To address these issues
this paper proposes an adaptive spectral graph wavelet convolutional neural network （ASGWCN） fault diagnosis method for the bearings in large rotating equipment
such as aircraft engines and wind turbines. The method considers the interactions between multiple sensors and converts vibration signals into graph-structured data. It integrates the adaptive spectral graph wavelet and re-weighted wavelet coefficient strategies
optimized by the Cheetah optimization algorithm
into the graph convolution layers
thereby constructing the ASGWCN. This approach dynamically adjusts the design parameters and decomposition levels of the spectral graph wavelet based on the characteristics of the vibration signal
enabling efficient multi-scale feature extraction. The different scale features are assigned different weights
enhancing the signal denoising and weak feature extraction capabilities. The method achieves end-to-end bearing fault diagnosis in situ under high noise environments
while also providing improved interpretability for the graph convolution process. The experimental results show that the proposed method exhibits excellent diagnostic performance and robustness under both high noise and complex working conditions.
关键词
Keywords
references
LIU J Y, WAN L, XIE F Q, et al.Cross-machine deep subdomain adaptation network for wind turbines fault diagnosis[J]. Mechanical systems and signal processing, 2024, 210: 111151.
徐硕, 邓艾东, 杨宏强, 等. 基于改进残差网络的旋转机械故障诊断[J]. 太阳能学报, 2023, 44(7): 409-418.
LIU J Y, WANG X S, WU S J, et al.Wind turbine fault detection based on deep residual networks[J]. Expert systems with applications, 2023, 213: 119102.
张辉, 戈宝军, 韩斌, 等. 基于GAF-CapsNet的电机轴承故障诊断方法[J]. 电工技术学报, 2023, 38(10): 2675-2685.
孔晓佳, 孟良, 许同乐, 等. 基于AVMD和WDK的风电齿轮箱轴承复合故障诊断方法研究[J]. 太阳能学报, 2022, 43(12): 206-213.
LI T F, ZHOU Z, LI S N, et al.The emerging graph neural networks for intelligent fault diagnostics and prognostics: a guideline and a benchmark study[J]. Mechanical systems and signal processing, 2022, 168: 108653.
MICHAU G, FRUSQUE G, FINK O.Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series[J]. Proceedings of the national academy of sciences of the United States of America, 2022, 119(8): e2106598119.
CHEN Z W, XU J M, PENG T, et al.Graph convolutional network-based method for fault diagnosis using a hybrid of measurement and prior knowledge[J]. IEEE transactions on cybernetics, 2022, 52(9): 9157-9169.
YU X X, TANG B P, ZHANG K.Fault diagnosis of wind turbine gearbox using a novel method of fast deep graph convolutional networks[J]. IEEE transactions on instrumentation and measurement, 2021, 70: 6502714.
SU G X, WANG H C, ZHANG Y, et al.Simple and deep graph attention networks[J]. Knowledge-based systems, 2024, 293: 111649.
DEFFERRARD M, BRESSON X, VANDERGHEYNST P. Convolutional neural networks on graphs with fast localized spectral filtering[J]. arXiv, 2016: arXiv: 1606.09375.
董鑫, 李国龙, 何坤, 等. 谱图小波阈值降噪及其在滚刀主轴振动信号分析中的应用[J]. 机械工程学报, 2020, 56(11): 96-107.
DONG X, LI, G, JIA Y C,et al. Non-iterative denoising algorithm for mechanical vibration signal using spectral graph wavelet transform and detrended fluctuation analysis. [J]. Mechanical systems & signal processing,2021,Vol.149: 107202.
CHENG J, YANG Y, LI X, et al.Symplectic geometry packet decomposition and its applications to gear fault diagnosis[J]. Mechanical systems and signal processing, 2022, 174: 109096.
AKBARI M A, ZARE M, AZIZIPANAH-ABARGHOOEE R, et al.The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems[J]. Scientific reports, 2022, 12: 10953.
HAMMOND D K, VANDERGHEYNST P, GRIBONVAL R.Wavelets on graphs via spectral graph theory[J]. Applied and computational harmonic analysis, 2011, 30(2): 129-150.
谭启瑜, 马萍, 张宏立. 基于图卷积神经网络的滚动轴承故障诊断[J]. 噪声与振动控制, 2023, 43(6): 101-108, 116.
HOU L, YI H M, JIN Y H, et al.Inter-shaft bearing fault diagnosis based on aero-engine system: a benchmarking dataset study[J]. Journal of dynamics, monitoring and diagnostics, 2023, 2(4): 207-289
HAMILTON W L, YING R, LESKOVEC J.Inductive representation learning on large graphs[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, California, USA, 2017: 1025-1035.
SHAO S Y, MCALEER S, YAN R Q, et al.Highly accurate machine fault diagnosis using deep transfer learning[J]. IEEE transactions on industrial informatics, 2019, 15(4): 2446-2455.
0
浏览量
1
下载量
0
CSCD
- Meta-XML下载
- BibTeX下载
- Endnote下载
- RefMan 下载
- NoteExpress下载
- NoteFirst下载
关联资源
相关文章
相关作者
相关机构
AI问答- 技术支持由北京北大方正电子有限公司提供 京ICP备09064830号-19
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies