奇异值分解用于早期摩擦故障诊断的研究

曹志伟; 黄金军; 任杰; 钱文浩; 杨建刚

doi:10.19805/j.cnki.jcspe.2026.250486

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奇异值分解用于早期摩擦故障诊断的研究

数字化与智能化 | 更新时间：2026-09-15

- 奇异值分解用于早期摩擦故障诊断的研究
- Research on the Application of Singular Value Decomposition in Early Friction Fault Diagnosis
- 动力工程学报 2026年46卷第9期页码：162-170
- 作者机构：
  
  1. 国网山东省电力公司电力科学研究院,山东,济南,250003
  2. 东南大学能源与环境学院,江苏,南京,210096
- 作者简介：
  
  曹志伟(1974—),男,山东济南人,正高级工程师,本科,研究方向为电机状态监测和诊断,E-mail:zw_cao@163.com。
- 基金信息：
  
  国家电网有限公司科技项目资助项目（5500-202416156A-1-1-ZN）
- DOI：10.19805/j.cnki.jcspe.2026.250486
  中图分类号： TK268.1
- 纸质出版：2026
- 稿件说明：
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曹志伟, 黄金军, 任杰, 钱文浩, 杨建刚. 奇异值分解用于早期摩擦故障诊断的研究[J]. 动力工程学报, 2026, 46(9): 162-170.
曹志伟, 黄金军, 任杰, 钱文浩, 杨建刚. 奇异值分解用于早期摩擦故障诊断的研究[J]. 动力工程学报, 2026, 46(9): 162-170. DOI： 10.19805/j.cnki.jcspe.2026.250486.

DOI： 10.19805/j.cnki.jcspe.2026.250486

摘要

为了提高早期摩擦故障诊断能力，引入奇异值分解方法。利用旋转机械振动信号周期性较强特点，由整周期采集到的信号构建Hankel矩阵，通过奇异值分解方法求取矩阵奇异值，并将奇异值分为大值区、中值区和小值区。结果表明：根据大值区奇异值可以重构出信号中主要的简谐周期分量，根据小值区奇异值可以重构信号中的噪声分量，根据中值区奇异值重构出的信号可以监测周期性冲击现象，而该特征为早期摩擦故障典型特征。结合摩擦故障实例进行分析，有效提取到了每周期2次的冲击特征，并进一步提取到了瞬时能量冲击现象，验证了该方法用于早期摩擦故障诊断的可行性。

Abstract

To improve the early friction fault diagnosis capability

the singular value decomposition method was introduced. By utilizing the characteristic of strong periodicity of rotating machinery vibration signals

a Hankel matrix was constructed from the signals collected in a full cycle. The singular values of the matrix were obtained through the singular value decomposition method

and the singular values were divided into a large-value region

a medium-value region and a small-value region. Results show that the main harmonic periodic components in the signal can be reconstructed according to the singular values in the large-value region

the noise components in the signal can be reconstructed according to the singular values in the small-value region

and the signal reconstructed from the singular values in the medium-value region can monitor the periodic impact phenomenon

which is a typical feature of early friction faults. Combined with the analysis of a friction fault case

the impact feature of twice per cycle is effectively extracted

and the instantaneous energy impact phenomenon is further extracted

which verifies the feasibility of this method for early friction fault diagnosis.

关键词

Keywords

references

刘新香, 顾煜炯, 马吉伟, 等. 基于声发射信号特征提取技术的滑动轴承故障诊断[J]. 汽轮机技术, 2024, 66(1): 1-6, 28, 52.LIU Xinxiang, GU Yujiong, MA Jiwei, et al. Sliding bearing fault diagnosis based on acoustic emission signal feature extraction technology[J]. Turbine Technology, 2024, 66(1): 1-6, 28, 52.

郝木明, 周芮, 孙彭涛, 等. 接触式机械密封启停过程摩擦特性实验研究[J]. 润滑与密封, 2023, 48(9): 41-46.HAO Muming, ZHOU Rui, SUN Pengtao, et al. Experimental study on friction characteristics of contact mechanical seal in start-up and stop stages[J]. Lubrication Engineering, 2023, 48(9): 41-46.

卢绪祥, 谭浩宇, 陈向民, 等. 基于声发射信号信息贴近度的滑动轴承润滑状态诊断方法[J]. 动力工程学报, 2019, 39(5): 373-379.LU Xuxiang, TAN Haoyu, CHEN Xiangmin, et al. Lubrication state diagnosis of journal bearings based on information exergy closeness degree of acoustic emission signals[J]. Journal of Chinese Society of Power Engineering, 2019, 39(5): 373-379.

宋伟, 高伟, 李敬豪, 等. 密封瓦引发振动及其与摩擦故障比较分析[J]. 汽轮机技术, 2024, 66(2): 113-116, 119.SONG Wei, GAO Wei, LI Jinghao, et al. Analysis of sealing bearing induced vibration and comparison with rub faults[J]. Turbine Technology, 2024, 66(2): 113-116, 119.

张恒, 张卫军, 胡启龙, 等. Arabelle型核电汽轮发电机轴系振动与密封瓦运行状态分析[J]. 热力发电, 2023, 52(10): 138-143.ZHANG Heng, ZHANG Weijun, HU Qilong, et al. Analyse on shafting vibration and operating state of sealing pad of Arabelle type nuclear power turbo generator[J]. Thermal Power Generation, 2023, 52(10): 138-143.

张卫军, 张恒, 胡启龙, 等. Arabelle型核电机组典型振动故障分析与处理对策[J]. 热力发电, 2022, 51(12): 172-178.ZHANG Weijun, ZHANG Heng, HU Qilong, et al. Analysis and treatment measures for typical vibration faults of Arabelle nuclear power turbo-generator unit[J]. Thermal Power Generation, 2022, 51(12): 172-178.

李振, 陈慧慧, 王洪凯, 等. 三代核电1 000 MW级汽轮机摩擦振动故障研究[J]. 原子能科学技术, 2022, 56(增刊1): 171-178.LI Zhen, CHEN Huihui, WANG Hongkai, et al. Research on rub induced vibration fault of 1 000 MW steam turbine on third generation nuclear power[J]. Atomic Energy Science and Technology, 2022, 56 (Sup1): 171-178.

邢芷恺, 刘永葆, 霍玉鑫, 等. 基于SVD和SSA-VMD降噪的轴承故障特征提取[J]. 热能动力工程, 2022, 37(9): 178-187.XING Zhikai, LIU Yongbao, HUO Yuxin, et al. Bearing fault feature extraction based on SVD and SSA-VMD de-noising[J]. Journal of Engineering for Thermal Energy and Power, 2022, 37(9): 178-187.

刘长良, 闫萧. 基于工况辨识和变分模态分解的风电机组滚动轴承故障诊断[J]. 动力工程学报, 2017, 37(4): 273-278, 334.LIU Changliang, YAN Xiao. Fault diagnosis of a wind turbine rolling bearing based on variational mode decomposition and condition identification[J]. Journal of Chinese Society of Power Engineering, 2017, 37(4): 273-278, 334.

胡璇, 李春, 叶柯华. 风力机齿轮箱振动信号分解方法研究[J]. 动力工程学报, 2021, 41(4): 323-329.HU Xuan, LI Chun, YE Kehua. Analysis on vibration signal decomposition methods of wind turbine gearbox[J]. Journal of Chinese Society of Power Engineering, 2021, 41(4): 323-329.

刘湘楠, 赵学智, 上官文斌. 强背景噪声振动信号中滚动轴承故障冲击特征提取[J]. 振动工程学报, 2021, 34(1): 202-210.LIU Xiangnan, ZHAO Xuezhi, SHANGGUAN Wenbin. The impact features extraction of rolling bearing under strong background noise[J]. Journal of Vibration Engineering, 2021, 34(1): 202-210.

赵学智, 叶邦彦, 陈统坚. 多分辨奇异值分解理论及其在信号处理和故障诊断中的应用[J]. 机械工程学报, 2010, 46(20): 64-75.ZHAO Xuezhi, YE Bangyan, CHEN Tongjian. Theory of multi-resolution singular value decomposition and its application to signal processing and fault diagnosis[J]. Journal of Mechanical Engineering, 2010, 46(20): 64-75.

张永强, 易亮. 基于奇异值分解的航空发动机转子碰摩故障特征提取方法[J]. 应用力学学报, 2019, 36(2): 260-266.ZHANG Yongqiang, YI Liang. The extraction of the fault features from aero-engine rotor contact-rubbing based on singular value decomposition[J]. Chinese Journal of Applied Mechanics, 2019, 36(2): 260-266.

陶然, 许有才, 邓方华, 等. 基于SVD优化LMD的电梯导靴振动信号故障特征提取[J]. 振动与冲击, 2017, 36(22): 166-171.TAO Ran, XU Youcai, DENG Fanghua, et al. Feature extraction of an elevator guide shoe vibration signal based on SVD optimizing LMD[J]. Journal of Vibration and Shock, 2017, 36(22): 166-171.

齐鹏, 范玉刚, 李葵. 基于SVD突变信息特征提取和VPMCD的故障诊断方法研究[J]. 云南大学学报(自然科学版), 2016, 38(2): 211-218.QI Peng, FAN Yugang, LI Kui. A study on fault diagnosis method based on transient feature extraction of SVD and VPMCD[J]. Journal of Yunnan University (Natural Sciences Edition), 2016, 38(2): 211-218.

李华, 刘韬, 伍星, 等. 相关奇异值比的SVD在轴承故障诊断中的应用[J]. 机械工程学报, 2021, 57(21): 138-149.LI Hua, LIU Tao, WU Xing, et al. Application of SVD based on correlated singular value ratio in bearing fault diagnosis[J]. Journal of Mechanical Engineering, 2021, 57(21): 138-149.

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.QI Yongsheng, LIU Fei, LI Yongting, et al. Compound fault diagnosis of wind turbine rolling bearing based on MK-MOMEDA and Teager energy operator[J]. Acta Energiae Solaris Sinica, 2021, 42(7): 297-307.

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