基于神经基拓展分析的风电机组齿轮箱故障预警

魏乐; 舒孝海; 陈远野; 房方

doi:10.19805/j.cnki.jcspe.2026.240732

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基于神经基拓展分析的风电机组齿轮箱故障预警

数字化与智能化 | 更新时间：2026-10-10

- 基于神经基拓展分析的风电机组齿轮箱故障预警
- Research on Wind Turbine Gearbox Fault Warning Method Basedon Neural Basis Expansion Analysis
- 动力工程学报 2026年46卷第2期页码：146-156
- 作者机构：
  
  华北电力大学控制与计算机工程学院,北京,102206
- 作者简介：
  
  魏乐(1976—),女,北京人,教授,博士,研究方向为风电机组状态监测与故障预警
  陈远野(通信作者),男,讲师,博士,E-mail:yuanyechen@ncepu.edu.cn
- 基金信息：
  
  华能集团总部科技资助项目(HNKJ29-H88):海上风电与智慧能源系统科技专项(一期)(HNKJ20-H88)
- DOI：10.19805/j.cnki.jcspe.2026.240732
  中图分类号：
- 网络首发：2026-02-10，
  
  纸质出版：2026-02-10
- 稿件说明：
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魏乐,舒孝海,陈远野,房方. 基于神经基拓展分析的风电机组齿轮箱故障预警动力工程学报, 2026, 46(2): 146-156 https://doi.

org/10.19805/j.cnki.jcspe.2026.240732
魏乐,舒孝海,陈远野,房方. 基于神经基拓展分析的风电机组齿轮箱故障预警动力工程学报, 2026, 46(2): 146-156 https://doi. DOI： 10.19805/j.cnki.jcspe.2026.240732.

org/10.19805/j.cnki.jcspe.2026.240732 DOI：

摘要

为解决现有齿轮箱故障预警方法中特征冗余、预测精度低、泛化能力弱及误报率高的问题

提出了一种基于神经基拓展分析的预警方法。该方法将Sigmoid函数曲线与基于密度的空间聚类算法相结合

从原始数据中提取正常监测数据;通过改进的灰色关联度算法为不同数据点赋予权重

深度挖掘变量信息

从高维监控与数据采集系统采集的数据中提取与齿轮箱油温相关的特征变量;利用局部非线性投影将目标信号分解为基函数

实现对齿轮箱状态变量的高精度预测;通过滑动窗口和置信区间设置故障阈值

减少误报率。最后

基于实际风场数据进行验证。结果表明:在齿轮箱状态预测中基于NBEATSx模型的故障预警方法显著提升了预测精度

较传统模型降低了误报率

具备提前数小时预知故障的能力

从而有效保障了风电机组的稳定运行。

Abstract

To address issues of feature redundancy

low prediction accuracy

weak generalization ability

and high false alarm rates in existing gearbox fault warning methods

a fault warning method based on neural basis expansion analysis was proposed. The method combined Sigmoid growth curve with a density-based spatial clustering algorithm to extract normal monitoring data from raw data. An improved grey relational analysis algorithm was used to assign weights to different data points

variable information was deeply explored. Feature variables related to the oil temperature of the gearbox were extracted from the data collected by the high-dimensional monitoring and data acquisition system. Local nonlinear projection was used to decompose the target signal into basis functions to predict the state variables of the gearbox accurately

fault thresholds were set by sliding window and confidence interval to reduce the false alarm rate. The method was verified based on actual wind field data. Results show that the fault prediction method based on the NBEATSx model significantly improves the prediction accuracy in the state prediction of gearboxes

reduces the false alarm rate compared to traditional models

and has the ability to predict faults several hours in advance

thereby effectively ensuring the stable operation of wind turbine units.

关键词

Keywords

references

苏健, 梁英波, 丁麟, 等. 碳中和目标下我国能源发展战略探讨[J]. 中国科学院院刊, 2021, 36(9):1001-1009.SU Jian, LIANG Yingbo, DING Lin, et al. Research on China's energy development strategy under carbon neutrality[J]. Bulletin of Chinese Academy of Sciences, 2021, 36(9):1001-1009.

GWEC. GWEC's global wind report 2024[R]. Belgium:GWEC, 2024.

陈雪峰, 郭艳婕, 许才彬, 等. 风电装备故障诊断与健康监测研究综述[J]. 中国机械工程, 2020, 31(2):175-189.CHEN Xuefeng, GUO Yanjie, XU Caibin, et al. Review of fault diagnosis and health monitoring for wind power equipment[J]. China Mechanical Engineering, 2020, 31(2):175-189.

黄荣舟, 汤宝平, 杨燕妮, 等. 基于长短时记忆网络融合SCADA数据的风电齿轮箱状态监测[J]. 太阳能学报, 2021, 42(1):235-239.HUANG Rongzhou, TANG Baoping, YANG Yanni, et al. Condition monitoring of wind turbine gearbox based on LSTM neural network fusing SCADA data[J]. Acta Energiae Solaris Sinica, 2021, 42(1):235-239.

CHENG Fangzhou, QU Liyan, QIAO Wei, et al. Enhanced particle filtering for bearing remaining useful life prediction of wind turbine drivetrain gearboxes[J]. IEEE Transactions on Industrial Electronics, 2019, 66(6):4738-4748.

WANG Haifeng, ZHAO Xingyu, WANG Weijun. Fault diagnosis and prediction of wind turbine gearbox based on a new hybrid model[J]. Environmental Science and Pollution Research, 2023, 30(9):24506-24520.

雷萌, 郭鹏, 刘博嵩. 基于自适应DBSCAN算法的风电机组异常数据识别研究[J]. 动力工程学报, 2021, 41(10):859-865.LEI Meng, GUO Peng, LIU Bosong. Research on abnormal data ldentification of wind turbines based on adaptive DBSCAN algorithm[J]. Journal of Chinese Society of Power Engineering, 2021, 41(10):859-865.

BHARDWAJ U, TEIXEIRA A P, SOARES C G, et al. Reliability prediction of an offshore wind turbine gearbox[J]. Renewable Energy, 2019, 141:693-706.

黄忠山, 田凌, 向东, 等. 基于PCA和SPC-动态神经网络的风电机组齿轮箱油温趋势预测[J]. 清华大学学报(自然科学版), 2018, 58(6):539-546.HUANG Zhongshan, TIAN Ling, XIANG Dong, et al. Prediction of oil temperature variations in a wind turbine gearbox based on PCA and an SPC-dynamic neural network hybrid[J]. Journal of Tsinghua University (Science and Technology), 2018, 58(6):539-546.

HE Ke, SU Zuqiang, TIAN Xiaoqing, et al. RUL prediction of wind turbine gearbox bearings based on self-calibration temporal convolutional network[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71:3501912.

苏向敬, 山衍浩, 周汶鑫, 等. 基于GRU和注意力机制的海上风机齿轮箱状态监测[J]. 电力系统保护与控制, 2021, 49(24):141-149.SU Xiangjing, SHAN Yanhao, ZHOU Wenxin, et al. GRU and attention mechanism-based condition monitoring of an offshore wind turbine gearbox[J]. Power System Protection and Control, 2021, 49(24):141-149.

张书瑶, 刘长良, 王梓齐, 等. 基于改进实例学习算法的风电机组齿轮箱状态监测[J]. 动力工程学报, 2024, 44(10):1620-1631.ZHANG Shuyao, LIU Changliang, WANG Ziqi, et al. Wind turbine gearbox condition monitoring based on improved instance learning algorithm[J]. Journal of Chinese Society of Power Engineering, 2024, 44(10):1620-1631.

景彤梅, 齐咏生, 刘利强, 等. 基于KECA-GRNN的风电机组齿轮箱状态监测与健康评估[J]. 太阳能学报, 2021, 42(6):400-408.JING Tongmei, QI Yongsheng, LIU Liqiang, et al. Condition monitoring and health assessment of wind turbine gearbox based on KECA-GRNN[J]. Acta Energiae Solaris Sinica, 2021, 42(6):400-408.

FANG Jinjie, YANG Linshan, WEN Xiaohu, et al. Ensemble learning using multivariate variational mode decomposition based on the transformer for multi-step-ahead streamflow forecasting[J]. Journal of Hydrology, 2024, 636:131275.

HUANG Xinyu, TANG Jun, SHEN Yongming. Long time series of ocean wave prediction based on PatchTST model[J]. Ocean Engineering, 2024, 301:117572.

ZHANG Jianwei, TAN Chunchen, CAI Zengyu, et al. Cellular traffic forecasting based on inverted transformer for mobile perception dual-level base station sleep control[J]. Ad Hoc Networks, 2024, 161:103505.

SOUTO H G, MORADI A. Introducing NBEATSx to realized volatility forecasting[J]. Expert Systems with Applications, 2024, 242:122802.

OLIVARES K G, CHALLU C, MARCJASZ G, et al. Neural basis expansion analysis with exogenous variables:forecasting electricity prices with NBEATSx[J]. International Journal of Forecasting, 2023, 39(2):884-900.

雍彬, 陈进, 张方红, 等. 基于门控循环网络融合多源数据的风电齿轮箱状态预警方法[J]. 太阳能学报, 2021, 42(8):421-425.YONG Bin, CHEN Jin, ZHANG Fanghong, et al. State warning of wind turbine gearbox based on gated recurrent unit network fusing multi-source data[J]. Acta Energiae Solaris Sinica, 2021, 42(8):421-425.

FU Yang, WANG Shuo, JIA Feng, et al. Two-stage cascaded high-precision early warning of wind turbine faults based on machine learning and data graphization[J]. Journal of Electrical Engineering & Technology, 2024, 19(3):1919-1931.

WANG Shaoke, ZHANG Zhaoyan, WANG Peiguang, et al. Failure warning of gearbox for wind turbine based on 3-median criterion and NSET[J]. Energy Reports, 2021, 7(Sup7):1182-1197.

向玲, 朱浩伟, 丁显, 等. 基于CAE与BiLSTM结合的风电机组齿轮箱故障预警方法研究[J]. 动力工程学报, 2022, 42(6):514-521.XIANG Ling, ZHU Haowei, DING Xian, et al. Research on fault warning method of wind turbine gearbox based on CAE and BiLSTM[J]. Journal of Chinese Society of Power Engineering, 2022, 42(6):514-521.

罗毅, 段明达. 基于IWOA-Transformer的磨煤机故障预警[J]. 动力工程学报, 2024, 44(6):939-946.LUO Yi, DUAN Mingda. Fault warning of coal mill based on IWOA-transformer[J]. Journal of Chinese Society of Power Engineering, 2024, 44(6):939-946.

KAVAKLI M, GUDMESTAD O T. Analysis and assessment of onshore and offshore wind turbines failures[J]. International Journal of Energy Production and Management, 2023, 8(1):27-34.

杨传光, 费胜巍. 基于Sigmoid函数的非对称式S型曲线机械臂加减速控制方法[J]. 东华大学学报(自然科学版), 2024, 50(5):118-126.YANG Chuanguang, FEI Shengwei. Asymmetric S-curve manipulator acceleration and deceleration control method based on Sigmoid function[J]. Journal of Donghua University (Natural Science), 2024, 50(5):118-126.

SU Siyu, SUN Youchao, PENG Chong, et al. Improved gray correlation analysis and combined prediction model for aviation accidents[J]. Engineering Computation, 2023, 40(7/8):1570-1592.

马良玉, 耿妍竹, 梁书源, 等. 基于Stacking多模型融合的风电机组齿轮箱油池温度异常预警[J]. 中国电机工程学报, 2023, 43(增刊1):242-251.MA Liangyu, GENG Yanzhu, LIANG Shuyuan, et al. Anomaly warning of wind turbine gearbox oil pool temperature based on stacking fusion of multiple models[J]. Proceedings of the CSEE, 2023, 43(Sup1):242-251.

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