RESEARCH ON FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON BNN-RA MODEL

Yu Ping; Song Ziqiong; Cao Jie; Chen Xiliang

doi:10.19912/j.0254-0096.tynxb.2023-1941

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

RESEARCH ON FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON BNN-RA MODEL

更新时间：2025-10-18

- RESEARCH ON FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON BNN-RA MODEL
- Vol. 46, Issue 3, Pages: 643-651(2025)
- 作者机构：
  
  兰州理工大学电气工程与信息工程学院,兰州 730050
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2023-1941
  CLC： TH133.3
- Published Online：07 April 2025，
  
  Published：2025
- 稿件说明：
移动端阅览
余萍, 宋紫琼, 曹洁, 陈息良. 基于BNN-RA模型的风电机组轴承故障诊断研究[J]. 太阳能学报, 2025, 46(3): 643-651.

Yu Ping, Song Ziqiong, Cao Jie, et al. RESEARCH ON FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON BNN-RA MODEL[J]. 2025, 46(3): 643-651.
余萍, 宋紫琼, 曹洁, 陈息良. 基于BNN-RA模型的风电机组轴承故障诊断研究[J]. 太阳能学报, 2025, 46(3): 643-651. DOI： 10.19912/j.0254-0096.tynxb.2023-1941.

Yu Ping, Song Ziqiong, Cao Jie, et al. RESEARCH ON FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON BNN-RA MODEL[J]. 2025, 46(3): 643-651. DOI： 10.19912/j.0254-0096.tynxb.2023-1941.

摘要

针对风电机组轴承故障诊断中特征提取困难

模型迭代速度慢

精度低的问题

该文提出一种基于改进二值化神经网络（BNN）的风电机组轴承故障诊断方法。首先采用格拉姆角场（GAF）将轴承振动信号转换为二维图像

以提高特征提取精度

然后结合深度残差网络和注意力机制构建BNN-RA（BNN+ Residual Network + Spatial attention network structure）故障诊断模型

实现轴承的高效故障诊断

最终通过美国凯斯西储大学（CWRU）与江南大学（JNU）公开的轴承数据集进行方法有效性验证。结果表明

该方法可有效提高网络迭代速度和诊断精度

模型在CWRU轴承数据集单一工况下迭代11次可达到收敛

故障诊断准确率达到99.20%

在两数据集的不同工况下平均准确率可达98.46%与97.6%。

Abstract

To address the challenges of feature extraction

slow model iteration

and low accuracy in diagnosing faults in wind turbine bearings

this paper introduces a diagnostic approach based on an enhanced version of the Binarized Neural Network （BNN） methodology. Firstly

the Gramian Angular Field （GAF） is utilized to transform the bearing vibration signal into a two-dimensional image

improving the accuracy of feature extraction. Next

the BNN-RA model （BNN + Residual Network + Spatial Attention Network） is constructed by integrating a deep residual network with an attention mechanism

enabling efficient fault diagnosis for bearings. The results demonstrate that the proposed method significantly enhances both network iteration speed and diagnostic accuracy. Specifically

the model achieves convergence after only 11 iterations under a single working condition of the CWRU bearing dataset

with fault diagnosis accuracy reaching 99.20%. Furthermore

the average accuracy across the two datasets are 98.46% and 97.60% under different operating conditions

respectively.

关键词

Keywords

references

张振海, 王维庆, 王海云, 等. 基于HCS-GWO-MSVM的风电机组齿轮箱复合故障诊断研究[J]. 太阳能学报, 2021, 42(10): 176-182.

齐咏生, 刘飞, 李永亭, 等. 基于MK-MOMEDA和Teager能量算子的风电机组滚动轴承复合故障诊断[J]. 太阳能学报, 2021, 42(7): 297-307.

LIU Z P, ZHANG L.A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings[J]. Measurement, 2020, 149: 107002.

武昆, 徐元博, 杨娜. 时变滤波经验模态分解与对称差分解析能量算子在轴承故障诊断中的应用[J]. 噪声与振动控制, 2020, 40(5): 101-107.

LIU C, CHENG G, LIU B, et al.Bearing fault diagnosis method with unknown variable speed based on multi-curve extraction and selection[J]. Measurement, 2020, 153: 107437.

LI C Z, ZHENG J D, PAN H Y, et al.Refined composite multivariate multiscale dispersion entropy and its application to fault diagnosis of rolling bearing[J]. IEEE access, 2019, 7: 47663-47673.

XU Q F, LU S X, JIA W Y, et al.Imbalanced fault diagnosis of rotating machinery via multi-domain feature extraction and cost-sensitive learning[J]. Journal of intelligent manufacturing, 2020, 31(6): 1467-1481.

ZHANG Y C, YU K, LEI Z H, et al.Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning[J]. Expert systems with applications, 2023, 232: 120854.

LI Z, WANG Y, MA J N.Fault diagnosis of motor bearings based on a convolutional long short-term memory network of Bayesian optimization[J]. IEEE access, 2021, 9: 97546-97556.

LEI Y G, YANG B, JIANG X W, et al.Applications of machine learning to machine fault diagnosis: a review and roadmap[J]. Mechanical systems and signal processing, 2020, 138: 106587.

TUERXUN W, XU C, GUO H Y, et al.Fault diagnosis of wind turbines based on a support vector machine optimized by the sparrow search algorithm[J]. IEEE access, 2021, 9: 69307-69315.

MUSHTAQ S, MANJURUL ISLAM M M, SOHAIB M. Deep learning aided data-driven fault diagnosis of rotatory machine: a comprehensive review[J]. Energies, 2021, 14(16): 5150.

CHEN X H, ZHANG B K, GAO D.Bearing fault diagnosis base on multi-scale CNN and LSTM model[J]. Journal of intelligent manufacturing, 2021, 32(4): 971-987.

WANG J J, LIANG Y Y, ZHENG Y H, et al.An integrated fault diagnosis and prognosis approach for predictive maintenance of wind turbine bearing with limited samples[J]. Renewable energy, 2020, 145: 642-650.

ZHANG L, ZHANG H, CAI G W.The multiclass fault diagnosis of wind turbine bearing based on multisource signal fusion and deep learning generative model[J]. IEEE transactions on instrumentation and measurement, 2022, 71: 3514212.

苏元浩, 孟良, 许同乐, 等. 不平衡数据集下优化WGAN的风电机组齿轮箱故障诊断方法[J]. 太阳能学报, 2022, 43(11): 148-155.

MENG L, SU Y H, KONG X J, et al.A probabilistic Bayesian parallel deep learning framework for wind turbine bearing fault diagnosis[J]. Sensors, 2022, 22(19): 7644.

FU Z X, ZHOU Z H, YUAN Y.Fault diagnosis of wind turbine main bearing in the condition of noise based on generative adversarial network[J]. Processes, 2022, 10(10): 2006.

陈起磊, 蒋亦悦, 唐瑶, 等. 基于时频图与改进图卷积神经网络的异步电机故障诊断方法[J]. 振动与冲击, 2022, 41(24): 241-248.

ZHANG X L, HAN P, XU L, et al.Research on bearing fault diagnosis of wind turbine gearbox based on 1DCNN-PSO-SVM[J]. IEEE access, 2020, 8: 192248-192258.

郭占广, 尹帅, 谢敬玲, 等. 基于胶囊神经网络的轴承故障诊断方法研究[J]. 自动化与仪表, 2022, 37(12): 49-53.

熊剑, 邓松, 时大方. 基于改进残差网络的滚动轴承故障诊断[J]. 轴承, 2020(11): 50-55.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

WIND TURBINE FAULT DIAGNOSIS METHOD BASED ON PARALLEL CONVOLUTIONAL NEURAL NETWORK

基于多熵融合和多尺度卷积神经网络的风电机组轴承故障诊断方法

Related Author

Meng Liang

Su Yuanhao

Xu Tongle

Kong Xiaojia

Lan Xiaosheng

Li Yunfeng

张伊杰

刘宝良

Related Institution

Mechanical Engineering School, Shandong University of Technology

School of Electrical Engineering and Information Engineering, Lanzhou University of Technology

School of Information Engineering, Lanzhou City University

School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China

Liaoning Key Laboratory of Wind Power Generation Technology, Shenyang 110870, China

AI问答

⁰