Service Status Evaluation of Wind Turbine Yaw Bearing Based on CMSATE and DTCAE

WANG Xiaolong; YANG Xiubin; LU Zhipeng; JIN Hanwei; ZHANG Bowen; TAO Zhenzhe; HAN Huilong

doi:10.19805/j.cnki.jcspe.2026.240649

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Service Status Evaluation of Wind Turbine Yaw Bearing Based on CMSATE and DTCAE

更新时间：2026-10-10

- Service Status Evaluation of Wind Turbine Yaw Bearing Based on CMSATE and DTCAE
- Chinese Journal of Power Engineering Vol. 46, Issue 1, Pages: 138-147(2026)
- 作者机构：
  
  华北电力大学机械工程系,河北,保定,071000
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.240649
  CLC：
- Online First：15 January 2026，
  
  Published：15 January 2026
- 稿件说明：
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王晓龙,杨秀彬,陆志鹏,金韩微,张博文,陶振哲,韩会龙. 基于CMSATE和DTCAE的风电机组偏航轴承服役状态评估动力工程学报, 2026, 46(1): 138-147 https://doi.

org/10.19805/j.cnki.jcspe.2026.240649
王晓龙,杨秀彬,陆志鹏,金韩微,张博文,陶振哲,韩会龙. 基于CMSATE和DTCAE的风电机组偏航轴承服役状态评估动力工程学报, 2026, 46(1): 138-147 https://doi. DOI： 10.19805/j.cnki.jcspe.2026.240649.

org/10.19805/j.cnki.jcspe.2026.240649 DOI：

摘要

针对难以准确检测风电机组偏航轴承早期退化时刻的问题

提出了一种基于复合多尺度符号注意熵和趋势约束自编码器的服役状态评估模型。首先利用复合多尺度符号注意熵方法从偏航轴承原始振动信号中提取多尺度退化特征

然后利用趋势约束自编码器将多尺度退化特征进行压缩降维

构造出能够准确反映偏航轴承服役状态的健康指标

最后结合切比雪夫不等式的自适应健康阈值策略实现偏航轴承服役状态评估。结果表明:该模型能够准确跟踪偏航轴承的运行状态

及时捕捉到初始退化时刻

实现偏航轴承服役状态的准确评估。

Abstract

In response to the problem of difficulty in accurately detecting early degradation of wind turbine yaw bearings

a service status evaluation model based on composite multiscale symbolic attention entropy and trend constrained autoencoder was proposed. Firstly

multiscale degradation features were extracted from the original vibration signals of yaw bearings by the composite multiscale symbolic attention entropy method. Secondly

the trend constrained autoencoder was used to compress and reduce the dimensionality of multiscale degradation features

to construct a health index that can accurately reflect the service status of yaw bearings. Finally

yaw bearing performance degradation was evaluated using adaptive health threshold strategy based on Chebyshev's inequality. Results show that the proposed model can accurately track the operational state of yaw bearings

promptly capture the initial degradation moment

and provide an accurate assessment of the service status of yaw bearings.

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references

张保成, 贾志刚, 于喆昌, 等. 小型风力发电机偏航系统动力学分析[J]. 太阳能学报, 2021, 42(5): 387-393.ZHANG Baocheng, JIA Zhigang, YU Zhechang, et al. Dynamic analysis of yaw system for small wind turbines[J]. Acta Energiae Solaris Sinica, 2021, 42(5): 387-393.

胡姚刚, 刘怀盛, 时萍萍, 等. 风电机组偏航系统故障诊断与寿命预测综述[J]. 中国电机工程学报, 2022, 42(13): 4871-4883.HU Yaogang, LIU Huaisheng, SHI Pingping, et al. Overview of fault diagnosis and life prediction for wind turbine yaw system[J]. Proceedings of the CSEE, 2022, 42(13): 4871-4883.

李忠信, 王大龙, 庄佳才, 等. 基于遗传模糊控制的风电机组偏航系统疲劳载荷研究[J]. 动力工程学报, 2022, 42(8): 745-752, 768.LI Zhongxin, WANG Dalong, ZHUANG Jiacai, et al. Research on fatigue suppression of wind turbine yaw system based on genetic fuzzy control[J]. Journal of Chinese Society of Power Engineering, 2022, 42(8): 745-752, 768.

CHEGINI S N, MANJILI M J H, BAGHERI A. New fault diagnosis approaches for detecting the bearing slight degradation[J]. Meccanica, 2020, 55(1): 261-286.

肖家丰, 董绍江, 汤宝平, 等. 基于PEDCC性能退化指标及MCRNN的滚动轴承寿命状态识别方法[J]. 振动与冲击, 2022, 41(24): 176-183, 233.XIAO Jiafeng, DONG Shaojiang, TANG Baoping, et al. Rolling bearing life state recognition based on a PEDCC performance degradation indicator and MCRNN[J]. Journal of Vibration and Shock, 2022, 41(24): 176-183, 233.

周建民, 熊文豪, 尹文豪, 等. 结合VMD符号熵与SVDD的滚动轴承性能退化评估[J]. 机械科学与技术, 2023, 42(1): 31-37.ZHOU Jianmin, XIONG Wenhao, YIN Wenhao, et al. Performance degradation assessment model of rolling bearings combining VMD symbol entropy and SVDD[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 31-37.

赵洪山, 李浪. 基于MCKD-EMD的风电机组轴承早期故障诊断方法[J]. 电力自动化设备, 2017, 37(2): 29-36. ZHAO Hongshan, LI Lang. Incipient bearing fault diagnosis based on MCKD-EMD for wind turbine[J]. Electric Power Automation Equipment, 2017, 37(2): 29-36.

YANG Jiawei, CHOUDHARY G I, RAHARDJA S, et al. Classification of interbeat interval time-series using attention entropy[J]. IEEE Transactions on Affective Computing, 2023, 14(1): 321-330.

HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.

RAJAGOPALAN V, RAY A. Symbolic time series analysis via wavelet-based partitioning[J]. Signal Processing, 2006, 86(11): 3309-3320.

LI Yongbo, WANG Xianzhi, SI Shubin, et al. Entropy based fault classification using the case western reserve university data: a benchmark study[J]. IEEE Transactions on Reliability, 2020, 69(2): 754-767.

NAMDARI A, LI Z S. A multiscale entropy-based long short term memory model for lithium-ion battery prognostics[C]// Proceedings of 2021 IEEE International Conference on Prognostics and Health Management. Detroit: IEEE, 2021: 1-6.

WU Jun, WU Chaoyong, CAO Shuai, et al. Degradation data-driven time-to-failure prognostics approach for rolling element bearings in electrical machines[J]. IEEE Transactions on Industrial Electronics, 2019, 66(1): 529-539.

张明, 冯坤, 江志农. 基于动态自学习阈值和趋势滤波的机械故障智能预警方法[J]. 振动与冲击, 2014, 33(24): 8-14.ZHANG Ming, FENG Kun, JIANG Zhinong. A mechanical fault early warning methodology based on dynamic self-learning threshold and trendfiltering techniques[J]. Journal of Vibration and Shock, 2014, 33(24): 8-14.

DING Peng, WANG Hua, DAI Yongfei, et al. MDCCS based multistage life prediction of slewing bearing with a novel performance description: an improved variational mode decomposition approach[J]. Experimental Techniques, 2019, 43(3): 341-358.

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