基于振动和转子电流深度融合的风电机组转频估计及故障诊断方法

王城宇; 万书亭; 张雄; 王萱; 慈铁军

doi:doi:10.19912/j.0254-0096.tynxb.2024-1628

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基于振动和转子电流深度融合的风电机组转频估计及故障诊断方法

更新时间：2026-02-06

- 基于振动和转子电流深度融合的风电机组转频估计及故障诊断方法
- 基于振动和转子电流深度融合的风电机组转频估计及故障诊断方法
- 太阳能学报 2026年47卷第1期页码：621-633
- 作者机构：
- 作者简介：
- 基金信息：
  
  国家自然科学基金（52275109）;河北省自然科学基金（E2022502007）;河北省研究生创新能力培养资助项目（CXZZBS2025196）
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1628
  中图分类号： TK83
- 网络首发：2026-02-05，
  
  纸质出版：2026-02-05
- 稿件说明：
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王城宇, 万书亭, 张雄, 等. 基于振动和转子电流深度融合的风电机组转频估计及故障诊断方法[J]. 太阳能学报, 2026,47(1):621-633.

王城宇, 万书亭, 张雄, et al. 基于振动和转子电流深度融合的风电机组转频估计及故障诊断方法[J]. 2026, 47(1): 621-633.
王城宇, 万书亭, 张雄, 等. 基于振动和转子电流深度融合的风电机组转频估计及故障诊断方法[J]. 太阳能学报, 2026,47(1):621-633. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1628.

王城宇, 万书亭, 张雄, et al. 基于振动和转子电流深度融合的风电机组转频估计及故障诊断方法[J]. 2026, 47(1): 621-633. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1628.

摘要

针对单一振动信号或电流信号估计风电机组瞬时频率精度不高的问题

提出基于振动信号和转子电流信号深度融合的转频估计及齿轮箱故障诊断方法

旨在提高风电机组齿轮箱故障诊断的准确性。首先

分别利用变分模态分解和低通滤波对振动信号和转子电流信号进行降噪处理

通过同步提取变换分别获得振动信号和转子电流信号的瞬时频率曲线

并对两种信号的时频曲线进行预处理

获得两条风电机组时频曲线;然后

提出DA-BLCNN深度网络模型

融合得到齿轮箱故障轴承的瞬时频率曲线;最后

采用阶次跟踪算法对齿轮箱振动信号进行等角度重采样

实现对齿轮箱的故障检测。变转速工况下的齿轮箱轴承故障实验验证结果表明

所提方法可有效实现齿轮箱故障诊断。

Abstract

To address the issue of low accuracy in estimating the instantaneous frequency of wind turbines using a single vibration or current signal

this paper proposes a method for rotational frequency estimation and gearbox fault diagnosis based on the deep integration of vibration and rotor current signals. The goal is to improve the accuracy of gearbox fault diagnosis in wind turbines. Firstly

the vibration and rotor current signals are denoised using the Variational Mode Decomposition method and low-pass filtering

respectively. The instantaneous frequency curves of the vibration and rotor current signals are then obtained using synchroextracting transform

and the time-frequency curves of both signals are preprocessed to obtain two time-frequency curves of the wind turbine. Subsequently

a DA-BLCNN deep network model is proposed to fuse the instantaneous frequency curves of the gearbox fault bearing. Finally

an order tracking algorithm is used to resample the gearbox vibration signal at equal angles

enabling fault detection of the gearbox. Through experimental validation of bearing faults in the gearbox under varying speeds

it can be concluded that the method proposed in this paper can effectively achieve fault diagnosis of the gearbox.

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references

马天霆, 孙良海, 韩冰, 等. 基于部分域适应的旋转机械跨工况故障诊断[J]. 太阳能学报, 2024, 45(6): 479-486.MA T T, SUN L H, HAN B, et al. Cross-working conditions fault diagnosis of rotating machinery based on partial domain adaptation[J]. Acta energiae solaris sinica, 2024, 45(6): 479-486.

WANG J, CHENG F Z, QIAO W, et al.Multiscale filtering reconstruction for wind turbine gearbox fault diagnosis under varying-speed and noisy conditions[J]. IEEE transactions on industrial electronics, 2018, 65(5): 4268-4278.

YU X L, HUANGFU Y F, HE Q B, et al.Gearbox fault diagnosis under nonstationary condition using nonlinear chirp components extracted from bearing force[J]. Mechanical systems and signal processing, 2022, 180: 109440.

俞昆, 罗志涛, 李鸿飞, 等. 广义参数化同步压缩变换及其在旋转机械振动信号中的应用[J]. 机械工程学报, 2019, 55(11): 149-159.YU K, LUO Z T, LI H F, et al. General parameterized synchrosqueezing transform and its application in rotating machinery vibration signal[J]. Journal of mechanical engineering, 2019, 55(11): 149-159.

陈龙, 史文库, 张曙光, 等. 基于改进型峰值搜索法的变速箱振动阶比分析[J]. 振动测试与诊断, 2020, 40(6): 1071-1076, 1228.CHEN L, SHI W K, ZHANG S G, et al. Order tracking of automobile gearbox in acceleration condition based on improved peak search algorithm[J]. Journal of vibration, measurement & diagnosis, 2020, 40(6): 1071-1076, 1228.

李志农, 刘跃凡, 胡志峰, 等. 经验小波变换-同步提取及其在滚动轴承故障诊断中的应用[J]. 振动工程学报, 2021, 34(6): 1284-1292.LI Z N, LIU Y F, HU Z F, et al. Empirical wavelet transform-synchroextracting transform and its applications in fault diagnosis of rolling bearing[J]. Journal of vibration engineering, 2021, 34(6): 1284-1292.

张焱, 何姝钡, 王平, 等. 无转速计下变工况滚动轴承故障特征量化表征提取[J]. 仪器仪表学报, 2021, 42(8): 104-114.ZHANG Y, HE S B, WANG P, et al. Tacholess quantitative characterization of rolling bearing fault feature under varying conditions[J]. Chinese journal of scientific instrument, 2021, 42(8): 104-114.

NIU J H, WANG X X, LU S L, et al.Motor bearing fault diagnosis based on frequency conversion estimation and order analysis[J]. Procedia manufacturing, 2020, 49: 160-165.

WANG J, PENG Y Y, QIAO W.Current-aided order tracking of vibration signals for bearing fault diagnosis of direct-drive wind turbines[J]. IEEE transactions on industrial electronics, 2016, 63(10): 6336-6346.

WANG X X, LU S L, CHEN K, et al.Bearing fault diagnosis of switched reluctance motor in electric vehicle powertrain via multisensor data fusion[J]. IEEE transactions on industrial informatics, 2022, 18(4): 2452-2464.

程侃如. 考虑风速时空分布的风电机组叶轮不平衡故障特征分析[D]. 北京: 华北电力大学, 2020.CHENG K R. Characteristic analysis of blade mass imbalance fault of wind turbines considering spatiotemporal distribution of wind speed[D]. Beijing: North China Electric Power University, 2020.

张琰妮, 史加荣, 李津, 等. 融合残差与VMD-ELM-LSTM的短期风速预测[J]. 太阳能学报, 2023, 44(9): 340-347.ZHANG Y N, SHI J R, LI J, et al. Short-term wind speed prediction based on residual and VMD-ELM-LSTM[J]. Acta energiae solaris sinica, 2023, 44(9): 340-347.

WAN S T, ZHAO X Y, WANG Y J, et al.A fault diagnosis method for variable speed gearbox bearing based on SET improved multi-source ridge line[J]. Measurement, 2023, 214: 112758.

马芳, 李慧, 王黎钦. 基于改进局部峰值法的轴承滚动体及内圈转速同步提取[J]. 轴承, 2025(7): 97-103.MA F, LI H, WANG L Q. Synchronous extraction of bearing rolling element and inner ring speeds based on improved local peak method[J]. Bearing, 2025(7): 97-103.

徐磊, 丁康, 何国林, 等. 一种极值搜索的齿轮系统振动信号阶次跟踪方法[J]. 振动工程学报, 2023, 36(3): 837-844.XU L, DING K, HE G L, et al. An order tracking method for gearbox vibration signal based on searching extremums[J]. Journal of vibration engineering, 2023, 36(3): 837-844.

刘玉鑫, 武文博, 张雄, 等. 基于HHO-CNN的轴承故障诊断方法研究[J]. 河北大学学报(自然科学版), 2023, 43(6): 571-583.LIU Y X, WU W B, ZHANG X, et al. Fault detection method of bearings based on HHO-CNN[J]. Journal of Hebei University (natural science edition), 2023, 43(6): 571-583.

SUN L X, HE J, ZHANG L T.CASF-MNet: multi-scale network with cross attention mechanism and spatial dimension feature fusion for maize leaf disease detection[J]. Crop protection, 2024, 180: 106667.

姜建国, 杨效岩, 毕洪波. 基于VMD-FE-CNN-BiLSTM的短期光伏发电功率预测[J]. 太阳能学报, 2024, 45(7): 462-473.JIANG J G, YANG X Y, BI H B. Photovoltaic power forecasting method based on VMD-FE-CNN-BiLSTM[J]. Acta energiae solaris sinica, 2024, 45(7): 462-473.

万书亭, 王燕杰, 张雄, 等. 时变工况的风电机组齿轮箱无转速计阶次跟踪方法研究[J]. 振动工程学报, 2023, 36(1): 266-279.WAN S T, WANG Y J, ZHANG X, et al. Tacho-less order tracking method of wind turbine gearbox under time-varying conditions[J]. Journal of vibration engineering, 2023, 36(1): 266-279.

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