机械工程学报 ›› 2025, Vol. 61 ›› Issue (1): 172-186.doi: 10.3901/JME.2025.01.172
张春林1, 吴允恒1, 蔡克燊1, 冯亚东2, 万方义1, 张安1
收稿日期:2023-12-12
修回日期:2024-06-20
出版日期:2025-01-05
发布日期:2025-02-26
作者简介:张春林,男,1988年出生,博士,副教授,硕士研究生导师。主要研究方向为飞行器振动分析与故障诊断、机载激光主动噪声控制。E-mail:zchunlin@nwpu.edu.cn基金资助:ZHANG Chunlin1, WU Yunheng1, CAI Keshen1, FENG Yadong2, WAN Fangyi1, ZHANG An1
Received:2023-12-12
Revised:2024-06-20
Online:2025-01-05
Published:2025-02-26
摘要: 针对变转速工况下滚动轴承非周期性故障冲击特征信号高保真提取问题,提出改进Morlet连续小波变换增强的非凸正则项稀疏分解方法。通过引入波形调节因子构造的改进Morlet小波基函数具有振荡属性可调的特性,能够匹配具有不同波形特征的故障冲击信号。将定转速下采用的包络谐噪比引入变转速工况,提出角度域包络谐噪比指标,实现对波形调节因子及阈值参数的优化。在此基础上,将改进Morlet连续小波变换与广义最小最大非凸正则项相结合形成稀疏分解模型;相较于离散小波变换,改进Morlet连续小波变换更容易将非周期性冲击型故障信号映射到时频稀疏域,进而通过稀疏模型求解实现非周期性故障冲击信号的提取。通过仿真信号及实验数据对该方法的有效性进行了验证,并与传统阈值降噪、频带滤波、基于品质因子可调小波稀疏分解等方法进行了比较。结果表明,所提方法能够有效提取出变转速工况下滚动轴承非周期性故障冲击特征信号。
中图分类号:
张春林, 吴允恒, 蔡克燊, 冯亚东, 万方义, 张安. 基于改进连续小波变换增强非凸正则项稀疏分解的滚动轴承变转速故障冲击特征提取方法[J]. 机械工程学报, 2025, 61(1): 172-186.
ZHANG Chunlin, WU Yunheng, CAI Keshen, FENG Yadong, WAN Fangyi, ZHANG An. Fault Transients Extraction of Rolling Bearings under Varying Speed via Modified Continuous Wavelet Transform Enhanced Nonconvex Sparse Representation[J]. Journal of Mechanical Engineering, 2025, 61(1): 172-186.
| [1] 王腾,李锋,罗玲,等. 基于双尺度柔性原型迁移网络的空间滚动轴承寿命阶段识别[J]. 机械工程学报,2022,58(21):114-125. WANG Teng,LI Feng,LUO Ling,et al. Life state recognition of space rolling bearings based on dual scale flexible prototype transfer network[J]. Journal of Mechanical Engineering,2022,58(21):114-125. [2] WANG Hui,WANG Zelin,QU Zhiguo,et al. Deep-learning accelerating topology optimization of three-dimensional coolant channels for flow and heat transfer in a proton exchange membrane fuel[J]. Applied Energy,2023,352:121889. [3] 江星星,彭德民,沈长青,等. 快速固有成分滤波特征融合的轴承故障诊断方法[J]. 机械工程学报,2022,58(22):129-139. JIANG Xingxing,PENG Demin,SHEN Changqing,et al. Feature fusion of fast intrinsic component filtering for bearing fault diagnosis[J]. Journal of Mechanical Engineering,2022,58(22):129-139. [4] HOU Wenbo,ZHANG Chunlin,JIANG Yunqian,et al. A new bearing fault diagnosis method via simulation data driving transfer learning without target fault data[J]. Measurement,2023,215:112879. [5] ZHAO Ke,JIA Feng,SHAO Haidong. A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains[J]. Knowledge-Based Systems,2023,262:110203. [6] 顾晓辉,杨绍普,刘文朋,等. 高速列车轴箱轴承健康监测与故障诊断研究综述[J]. 力学学报,2022,54(7):1780-1796. GU Xiaohui,YANG Shaopu,LIU Wenpeng,et al. Review of health monitoring and fault diagnosis of axle-box bearing of high-speed train[J]. Chinese Journal of Theoretical and Applied Mechanics,2022,54(7):1780-1796. [7] CUI Qianwen,ZHU Liangyu,FENG Huanqin,et al. Intelligent fault quantitative identification via the improved deep deterministic policy gradient (DDPG) algorithm accompanied with imbalanced sample[J]. IEEE Transactions on Instrumentation and Measurement,2023,72:1-13. [8] 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 Zhinong,LIU Yuefan,HU Zhifeng,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. [9] 邵海东,李伟,刘翊,等. 时变转速下基于双阈值注意力生成对抗网络和小样本的转子-轴承系统故障诊断[J]. 机械工程学报,2023,59(12):215-224. SHAO Haidong,LI Wei,LIU Yi,et al. Fault diagnosis of rotor-bearing system under time-varying speeds by using dual-threshold attention-embeded GAN and small samples[J]. Journal of Mechanical Engineering,2023,59(12):215-224. [10] 胡春生,李国利,赵勇,等. 变工况滚动轴承故障诊断方法综述[J]. 计算机工程与应用,2022,58(18):26-42. HU Chunsheng,LI Guoli,ZHAO Yong,et al. Summary of fault diagnosis methods for rolling bearings under variable working conditions[J]. Computer Engineering and Applications,2022,58(18):26-42. [11] 王晓龙,唐贵基,何玉灵. 基于COT-SSD的变转速滚动轴承微弱故障诊断[J]. 电力自动化设备,2019,39(5):187-193. WANG Xiaolong,TANG Guiji,HE Yuling. Weak fault diagnosis for rolling bearing based on COT-SSD under variable rotating speed[J]. Electric Power Automation Equipment,2019,39(5):187-193. [12] ABBOUD D,BAUDIN S,ANTONI J,et al. The spectral analysis of cyclo-non-stationary signals[J]. Mechanical Systems and Signal Processing,2016,75:280-300. [13] 杨新敏,郭瑜,华健翔. 基于阶频谱相干的变转速滚动轴承内外圈复合故障特征分离提取[J]. 振动与冲击,2022,41(22):211-218. YANG Xinmin,GUO Yu,HUA Jianxiang. Feature separation and extraction of compound faults of inner and outer rings of rolling bearings at variable speed based on order-frequency spectral coherence[J]. Journal of Vibration and Shock,2022,41(22):211-218. [14] 孙鑫威,纪爱敏,杜占涛,等. 动车组齿轮箱滚动轴承变转速故障诊断方法[J]. 哈尔滨工业大学学报,2023,55(1):106-115. SUN Xinwei,JI Aimin,DU Zhantao,et al. Fault diagnosis method for variable speed of rolling bearing in EMU gearbox[J]. Journal of Harbin Institude of Technology,2023,55(1):106-115. [15] ZHANG Kun,XU Yonggang,LIAO Zhiqiang,et al. A novel fast entrogram and its applications in rolling bearing fault diagnosis[J]. Mechanical Systems and Signal Processing,2021,154:107582. [16] FENG Zhipeng,CHEN Xiaowang,WANG Tianyang. Time-varying demodulation analysis for rolling bearing fault diagnosis under variable speed conditions[J]. Journal of Sound and Vibration,2017,400:71-85. [17] 柯伟,金仲平,吕信策,等. 二阶同步提取Chirplet变换及应用于变转速滚动轴承故障诊断[J]. 机床与液压,2022,50(14):176-181. KE Wei,JIN Zhongping,LÜ Xince,et al. Second-order synchroextracting chirplet transform and its application to the fault diagnosis of variable speed rolling bearing[J]. Machine Tool & Hydraulics,2022,50(14):176-181. [18] YANG Jianhua,WU Chengjin,SHAN Zhen,et al. Extraction and enhancement of unknown bearing fault feature in the strong noise under variable speed condition[J]. Measurement Science and Technology,2021,32:105021. [19] YU Xiao,LIANG Zhongting,WANG Youjie,et al. A wavelet packet transform-based deep feature transfer learning method for bearing fault diagnosis under different working conditions[J]. Measurement,2022,201:111597. [20] CHEN Shaowen,WANG Shibin,AN botao,et al. Instantaneous frequency band and synchrosqueezing in time-frequency analysis[J]. IEEE Transactions on Signal Processing,2023,71:539-554. [21] 吴红安,吕勇,易灿灿,等. 窗口伸缩优化的同步压缩算法及其在变转速工况瞬时频率估计上的应用[J]. 中国机械工程,2022,33(1):34-44. WU Hongan,LÜ Yong,YI Cancan,et al. Synchrosqueezing algorithm for window extension and compression optimization and its applications in Instantaneous frequency estimation under variable speed conditions[J]. China Mechanical Engineering,2022,33(1):34-44. [22] 罗洁思,黄巧园. 基于瞬时故障特征频率曲线提取的变转速滚动轴承故障诊断[J]. 轴承,2023,521(4):87-92. LUO Jiesi,HUANG Qiaoyuan. Fault diagnosis for variable speed rolling bearings based on instantaneous fault characteristic frequency curve extraction[J]. Bearing,2023,521(4):87-92. [23] ABBOUD D,ANTONI J. Order-frequency analysis of machine signals[J]. Mechanical Systems and Signal Processing,2017,87:229-258. [24] 石娟娟,花泽晖,沈长青,等. 广义瞬时速度同步化分步解调变换及其对旋转机械振动信号分析[J]. 振动与冲击,2021,40(24):1-11+21. SHI Juanjuan,HUA Zehui,SHEN Changqing,et al. A generalized instantaneous-frequency-estimation-free stepwise demodulation transform and its application in vibration signal analysis of rotating machinery[J]. Journal of Vibration and Shock,2021,40(24):1-11+21. [25] FENG Zhipeng,ZHU Wenying,ZHANG Dong. Time-frequency demodulation analysis via Vold-Kalman filter for wind turbine planetary gearbox fault diagnosis under nonstationary speeds[J]. Mechanical Systems and Signal Processing,2019,128:93-109. [26] 皮维,于德介,彭富强. 基于多尺度线调频基稀疏信号分解的广义解调方法及其在齿轮故障诊断中的应用[J]. 机械工程学报,2010,46(15):59-64,70. PI Wei,YU Dejie,PENG Fuqiang. Generalized demodulation method based on multi-scale chirplet and sparse signal decomposition and its application to gear fault diagnosis[J]. Journal of Mechanical Engineering,2010,46(15):59-64,70. [27] WANG Ran,FANG Haitao,YU Longjing,et al. Sparse and low-rank decomposition of the time–frequency representation for bearing fault diagnosis under variable speed conditions[J]. ISA Transactions,2022,128:579-598. [28] ZHANG Dingcheng,ENTEZANI M,STEWART E,et al. Wayside acoustic detection of train bearings based on an enhanced spline-kernelled chirplet transform[J]. Journal of Sound and Vibration,2020,480:115401. [29] FENG Zhipeng,ZHOU Yakai,ZUO Mingjian,et al. Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples[J]. Measurement,2017,103:106-132. [30] ZHANG Chunlin,QIANG Yudong,HOU Wenbo,et al. High-fidelity fault signature extraction of rolling bearings via nonconvex regularized sparse representation enhanced by flexible analytical wavelet transform[J]. Structural Health Monitoring,2024. DOI:10.1177/ 14759217231203240. [31] 薛红涛,丁殿勇,李汭铖,等. 基于分量加权重构和稀疏NMF的轮毂电机轴承复合故障特征提取方法[J]. 机械工程学报,2023,59(9):146-156. XUE Hongtao,DING Dianyong,LI Ruicheng,et al. Feature extraction method based on component weighted reconstruction and sparse NMF for bearing compound faults of in-wheel motor[J]. Journal of Mechanical Engineering,2023,59(9):146-156. [32] WANG Shibin,SELESNICK I,CAI Gaigai,et al. Nonconvex sparse regularization and convex optimization for bearing fault diagnosis[J]. IEEE Transactions on Industrial Electronics,2018,65(9):7332-7342. [33] QIN Yi. A new family of model-based impulsive wavelets and their sparse representation for rolling bearing fault diagnosis[J]. IEEE Transactions on Industrial Electronics,2018,65(3):2716-2726. [34] SUN Ruobin,YANG Zhibo,ZHAI Zhi,et al. Sparse representation based on parametric impulsive dictionary design for bearing fault diagnosis[J]. Mechanical Systems and Signal Processing,2019,122:737-753. [35] 吴楠,石娟娟,江星星,等. 基于SALSA优化算法的变转速轴承故障特征稀疏表示方法研究[J]. 机械设计与制造工程,2018,47(6):17-21. WU Nan,SHI Juanjuan,JIANG Xingxing,et al. The sparse representation of feature extraction of bearing fault based on SALSA algorithm at varying speed condition[J]. Machine Design and Manufacturing Engineering,2018,47(6):17-21. [36] SELESNICK I. Sparse regularization via convex analysis[J]. IEEE Transactions on Signal Processing,2017,65(17):4481-4494. [37] TORRENCE C,COMPO G P. A practical guide to wavelet analysis[J]. Bulletin of the American Meteorological Society,1998,79(1):61-78. [38] XU Xiaoqiang,ZHAO Ming,LIN Jing,et al. Envelope harmonic-to-noise ratio for periodic impulses detection and its application to bearing diagnosis[J]. Measurement,2016,91:385-397. [39] HUANG Huan,BADDOUR N. Bearing vibration data collected under time-varying rotational speed conditions[J]. Data in Brief,2018,21:1745-1749. [40] HUANG Huan,BADDOUR N,LIANG Ming. Bearing fault diagnosis under unknown time-varying rotational speed conditions via multiple time-frequency curve extraction[J]. Journal of Sound and Vibration,2018,414:43-60. |
| [1] | 杨彬, 李雅宁, 雷亚国, 李响, 曹军义, 武通海. 迁移拓扑规划的机械设备群体协同智能诊断方法[J]. 机械工程学报, 2026, 62(4): 1-11. |
| [2] | 周坪, 周公博, 王晗宇, 王攀, 闫晓东, 李远博. 基于涡流热成像的钢丝绳表面缺陷红外视觉检测方法[J]. 机械工程学报, 2026, 62(4): 86-97. |
| [3] | 黄金凤, 王成城, 何宏亮, 王旭, 李奇, 杨康定, 王凯, 张飞斌, 秦朝烨, 褚福磊. 思维链范式智能运维文本基多模态智能体[J]. 机械工程学报, 2025, 61(23): 58-74. |
| [4] | 杨波, 申小玉, 王时龙, 何彦, 杜卡泽. 面向设备运维的人机物三元融合知识图谱构建方法[J]. 机械工程学报, 2025, 61(17): 215-232. |
| [5] | 肖 扬, 王华庆, 李华, 王庆锋. 基于精细复合缩放多尺度加权排列熵的跨域故障诊断方法[J]. 机械工程学报, 2025, 61(16): 28-39. |
| [6] | 孙世博, 袁静, 赵倩, 蒋会明, 魏颖. 内核噪声拓展自适应多元变分模态分解及机械复合故障诊断应用[J]. 机械工程学报, 2025, 61(11): 171-182. |
| [7] | 郑俊康, 王辉, 向家伟. 动力学模型驱动的RV减速器故障诊断方法[J]. 机械工程学报, 2025, 61(11): 194-207. |
| [8] | 李永杰, 张周锁, 罗欣. 带参考信号的频域盲解卷积算法及其在卫星微振动同频相关源定量辨识中的应用[J]. 机械工程学报, 2025, 61(10): 215-229. |
| [9] | 邵海东, 颜深, 刘政武, 肖一鸣, 韩特. 面向小样本场景的风机多增量故障诊断[J]. 机械工程学报, 2025, 61(10): 230-240. |
| [10] | 吴艳灵, 汤宝平, 邓蕾, 付豪. 低通筛选优化神经架构搜索的风电齿轮箱边缘侧故障诊断方法[J]. 机械工程学报, 2025, 61(7): 361-372. |
| [11] | 雷亚国, 李熹伟, 李响, 李乃鹏, 杨彬. 面向机械设备通用健康管理的智能运维大模型[J]. 机械工程学报, 2025, 61(6): 1-13. |
| [12] | 鲍宏, 王政, 陶璟, 李红真, 于随然, 宋培龙. 面向重用与装配复杂度均衡性的机电产品非合作博弈模块化设计方法[J]. 机械工程学报, 2025, 61(5): 285-296. |
| [13] | 李华, 王天杨, 张飞斌, 褚福磊. KurVMDPgram:一种用于旋转机械故障诊断的信号分解算法[J]. 机械工程学报, 2025, 61(4): 11-23. |
| [14] | 裴雪武, 李新宇, 高亮, 高艺平, 陈志敏. 复合故障退化情形下基于自适应非线性状态估计的装备状态监测健康指标[J]. 机械工程学报, 2025, 61(3): 119-129. |
| [15] | 王晨飞, 王晓力, 郑辰. 超高速空气动静压轴承-转子系统不平衡量在线识别研究[J]. 机械工程学报, 2025, 61(3): 337-346. |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||
