Page 199 - 《振动工程学报》2025年第9期
P. 199
第 9 期 张伟涛,等:宽转速范围航发主轴轴承振动数据集 2129
364. method for bearing fault diagnosis[J]. Journal of Harbin
ZHANG Feng, HU Yantao, SHI Xianfeng. Cyclic Wiener University of Science and Technology,2022,27(4):118-
filtering algorithm in discrete cosine transform domain for 124.
vibration signals[J]. Journal of Xi’an Technological University, [17] 黄扣,袁伟,陈红卫. 基于深度学习的轴承故障智能诊断
2015,35(5):360-364. 方法研究 [J]. 计算机与数字工程,2022,50(8):1827-
[6] 郝芳,王宏超. 改进循环维纳滤波器算法的滚动轴承复合 1832.
故障诊断 [J]. 中国工程机械学报,2018,16(4):371-376. HUANG Kou,YUAN Wei,CHEN Hongwei. Research on
HAO Fang, WANG Hongchao. Fault diagnosis of rolling bearing fault intelligent diagnosis method based on deep learn-
element bearing’ compound faults basing on improved cyclic ing[J]. Computer and Digital Engineering, 2022, 50( 8) :
Wiener filter algorithm[J]. Chinese Journal of Construction 1827-1832.
Machinery,2018,16(4):371-376. [18] 林诗麒,陈智丽,李宇鹏,等. 基于深度学习和多域决策
[7] Case Western Reserve University, Bearing Data Center. 融合的轴承故障智能诊断技术 [J]. 计算机集成制造系统,
Seeded fault test data[EB/OL]. (2016-05)[2025-08] http:// 2024,30(10):3708-3718.
csegroups.case.edu/bearingdatacenter/home. LIN Shiqi,CHEN Zhili,LI Yupeng,et al. Intelligent bear-
[8] BECHHOEFER E. A quick introduction to bearing envelope ing fault diagnosis technology based on deep learning and
analysis[EB/OL]. (2016)[2025-08] http://www.mfpt.org/Fault-
multi-domain decision fusion[J]. Computer Integrated Manu-
Data/Fault-Data.htm.Set.
facturing Systems,2024,30(10):3708-3718.
[9] LEE J, QIU H, YU G, et al. Rexnord technical services: bear- [19] 金江涛,许子非,李春,等. 基于深度学习与支持向量机
ing data set[EB/OL]. Moffett Field, CA: NASA Ames Prog-
的 滚 动 轴 承 故 障 诊 断 研究 [J]. 热 能 动 力 工 程 , 2022,
nostics Data Repository, (2007) [2025-08]. https://ti.arc.nasa.
37(6):176-184.
gov/tech/dash/groups/pcoe/prognostic-data-repository/.
JIN Jiangtao,XU Zifei,LI Chun,et al. Research on rolling
[10] DAGA A P,FASANA A,MARCHESIELLO S,et al. The
bearing fault diagnosis based on deep learning and support
politecnico di torino rolling bearing test rig:description and
vector machine[J]. Journal of Engineering for Thermal Energy
analysis of open access data[J]. Mechanical Systems and
and Power,2022,37(6):176-184.
Signal Processing,2019,120:252-273.
[20] 刘佳峰. 基于深度学习的端到端轴承故障智能诊断方法
[11] FEMTO-ST Institute. IEEE PHM 2012 data challenge
[D]. 株洲:湖南工业大学,2022.
[EB/OL]. (2012-05) [2025-08]. http://www.femto-st.fr/en/
LIU Jiafeng. End-to-end bearing fault intelligent diagnosis
Research-departments/AS2M/Research-groups/PHM/IEEE-
method based on deep learning[D]. Zhuzhou:Hunan Univer-
PHM-2012-Data-challenge.php.
sity of Technology,2022.
[12] University of Ottawa. ORAP-40: ottawa railway bearing aging
[21] 温江涛,闫常弘,孙洁娣,等. 基于压缩采集与深度学习
and performance dataset[EB/OL]. (2020) [2025-08]. https://
的轴承故障诊断方法 [J]. 仪器仪表学报,2018,39(1):
www.uottawa.ca/faculty-engineering/.
171-179.
[13] 张伟涛,纪晓凡,黄菊,等. 航发轴承复合故障诊断的循
WEN Jiangtao,YAN Changhong,SUN Jiedi,et al. Bear-
环 维 纳 滤 波 方法 [J]. 西 安 电 子 科 技 大 学 学 报 , 2022,
ing fault diagnosis method based on compressed acquisition
49(6):139-151.
and deep learning[J]. Journal of Scientific Instrument,2018,
ZHANG Weitao, JI Xiaofan, HUANG Ju, et al. Cyclic
39(1):171-179.
Wiener filtering for compound fault diagnosis of an aero-
[22] 宫文峰,陈辉,张美玲,等. 基于深度学习的电机轴承微
engine rolling element bearing[J]. Journal of Xidian Univer-
小故障智能诊断方法 [J]. 仪器仪表学报,2020,41(1):
sity,2022,49(6):139-151.
195-205.
[14] 宋晓承,岑跃峰,张宇来,等. 基于双通道深度学习的轴
GONG Wenfeng, CHEN Hui, ZHANG Meiling, et al.
承故障诊断研究 [J]. 机电工程,2023,40(7):988-998.
Intelligent diagnosis method for incipient fault of motor bear-
SONG Xiaocheng, CEN Yuefeng, ZHANG Yulai, et al.
ing based on deep learning[J]. Chinese Journal of Scientific
Bearing fault diagnosis based on two-channel deep learning[J].
Journal of Mechanical and Electrical Engineering, 2023, Instrument,2020,41(1):195-205.
[23] 张伟涛,崔丹,刘璐,等. 宽转速范围下的航发主轴轴承
40(7):988-998.
故障诊断方法 [J]. 振动与冲击,2023,42(5):253-262.
[15] 芦奕霏. 基于深度学习的轴承故障诊断方法研究 [D]. 南
ZHANG W T,CUI D,LIU L,et al. Fault diagnosis method
京:南京邮电大学,2022.
of aero engine main shaft rolling bearings in wide rotating
LU Yifei. Research on bearing fault diagnosis method based
speed range[J]. Journal of Vibration and Shock, 2023,
on deep learning[D]. Nanjing: Nanjing University of Posts
and Telecommunications,2022. 42(5):253-262.
[16] 柳秀,马善涛,谢怡宁,等. 面向轴承故障诊断的深度学
习方法 [J]. 哈尔滨理工大学学报,2022,27(4):118-124. 通信作者:张伟涛(1983—),男,博士,教授。
LIU Xiu, MA Shantao, XIE Yining, et al. Deep learning E-mail:zhwt-work@foxmail.com

