基于改进MW-KECA算法的风电齿轮箱故障诊断研究

刘轩; 霍忠堂; 李冰

doi:doi:10.19912/j.0254-0096.tynxb.2024-1641

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基于改进MW-KECA算法的风电齿轮箱故障诊断研究

更新时间：2025-12-17

- 基于改进MW-KECA算法的风电齿轮箱故障诊断研究
- Issue 10, Pages: 766-773(2025)
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1641
  CLC： M614
- Published Online：31 October 2025，
  
  Published：31 October 2025
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刘轩, 霍忠堂, 李冰. 基于改进MW-KECA算法的风电齿轮箱故障诊断研究[J]. 2025, (10): 766-773.
DOI：

刘轩, 霍忠堂, 李冰. 基于改进MW-KECA算法的风电齿轮箱故障诊断研究[J]. 2025, (10): 766-773. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1641.

摘要

针对风力发电生产过程中齿轮箱常发故障

结合工业过程多变量、高维度、非线性、数据关联度高、异常数据缓慢劣化等特点

采用滑动窗口-核熵主元分析法（MW-KECA）对齿轮箱劣变过程中机组相关振动数据进行监控

并对齿轮箱故障进行诊断。改善样本适应性

同时针对算法中核参数选择偶然性和随机性问题

引入鲸鱼优化算法进行改进。通过齿轮箱数据集进行仿真研究

并在滚动轴承实验台的故障诊断中实现不同故障类型的准确识别验证

通过对比分析

采用MW-KECA和鲸鱼算法识别结果可更高效和准确地预测故障时间

减小误报率。

Abstract

A Moving Window-Kernel Entropy Component Analysis （MW-KECA） approach was used to monitor the vibration data related to the units during the gearbox degradation process and diagnose gearbox faults in response to the frequent failures in gearboxes during the production process of wind power generation.This was done while considering the characteristics of industrial processes

such as multiple variables

high dimensionality

non-linearity

high data correlation

and slow deterioration of abnormal data.This method improves sample overfitting.Additionally

the Whale Optimization Algorithm （WOA） was introduced to address the issues of stochasticity and randomness in kernel parameter selection inherent in the algorithm.Simulation studies were conducted using gearbox datasets

and accurate identification and verification of different fault types were achieved in fault diagnosis on a rolling bearing test rig.Comparative analyses reveals that the adoption of MW-KECA combined with WOA results in more efficient and accurate fault time prediction

with a reduced false alarm rate.

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references

宋中越, 马姣姣, 甄冬, 等. 风力发电机齿轮箱高故障率原因的研究及改进措施[J]. 机床与液压, 2018, 46(1): 173-177.

SCHÖLKOPF B, SMOLA A, MÜLLER K R. Nonlinear component analysis as a kernel eigenvalue problem[J]. Neural computation, 1998, 10(5): 1299-1319.

JIANG Q C, YAN X F, LV Z M, et al.Fault detection in nonlinear chemical processes based on kernel entropy component analysis and angular structure[J]. Korean journal of chemical engineering, 2013, 30(6): 1181-1186.

郭金玉, 李涛, 李元. 基于滑动窗口KECA-SVM的非线性过程故障检测[J]. 沈阳大学学报(自然科学版), 2022, 34(1): 37-44.

许昕, 韩慧苗, 潘宏侠, 等. 基于多特征提取和KECA柴油机关键部件故障识别[J]. 电子测量技术, 2021, 44(19): 63-68.

祝志超, 吴定会, 岳远昌. 基于监督核熵成分分析的发动机磨损故障诊断[J]. 系统仿真学报, 2022, 34(1): 45-52.

彭开香, 张丽敏. 基于核典型相关性-熵成分分析的工业过程质量监测方法[J]. 控制与决策, 2021, 36(12): 2999-3006.

孙建平, 李雪, 刘轩, 等. 改进滑动窗口核主元分析算法[J]. 热力发电, 2018, 47(1): 100-105.

郭金玉, 于欢, 李元. 基于KPCA-SVM的相关和独立变量故障检测方法[J]. 深圳大学学报(理工版), 2023, 40(1): 14-21.

李榕, 申志, 李元. 基于核熵成分分析的工业过程多类型故障诊断[J]. 电子测量技术, 2023, 46(10): 40-45.

张伟, 许爱强, 平殿发. 基于一种自适应核学习的KECA子空间故障特征提取[J]. 北京理工大学学报, 2017, 37(8): 863-868, 874.

李英顺, 阚宏达, 王德彪, 等. 一种基于KPCA-WOA-SVM火控系统故障诊断方法[J]. 火炮发射与控制学报, 2023, 44(4): 14-19.

白丽丽. 齿轮传动系统关键零部件故障状态识别方法研究[D]. 太原: 太原理工大学, 2020.

MIRJALILI S, LEWIS A.The whale optimization algorithm[J]. Advances in engineering software, 2016, 95: 51-67.

谷晓娇, 陈长征. 基于VMD和QPSO-SR的风电机组轴承故障提取方法[J]. 太阳能学报, 2019, 40(10): 2946-2952.

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.

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Related Institution

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

School of Information Engineering, Lanzhou City University

School of Control and Computer Engineering, North China Electric Power University

National Engineering Research Center of Power Generation Control and Safety, School of Energy and Environment, Southeast University

China Energy Jiangsu Power Co., Ltd.

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