李继猛
  • 硕士生导师
  • 职 称 : 讲师(高校)
  • 学 科 :
    检测技术与自动化装置
  • 单 位 : 电气工程学院
学位 : 博士
学历 : 博士研究生毕业
出生年月 : 1984-07
入职时间 : 2014-07-14
办公地点 : 电气馆A309-2
毕业院校 : 西安交通大学
电子信箱 :
科学研究

研究领域

  • 机械系统动态信号分析与处理;随机共振理论研究及应用;大型装备运行状态监测与智能诊断


    发表论文:

    • Jimeng Li, Xifeng Yao,  Xiangdong Wang, Qingwen Yu, Yungang Zhang. Multiscale local features learning based on BP neural network for rolling bearing intelligent fault diagnosis[J]. Measurement, 2020, https://doi.org/10.1016/j.measurement.2019.107419.  (SCI,IF 2.791)

    • Jimeng Li, Hui Wang, Xiangdong Wang, Yungang Zhang. Rolling bearing fault diagnosis based on improved adaptive parameterless empirical wavelet transform and sparse denoising[J]. Measurement, 2020, https://doi.org/10.1016/j.measurement.2019.107392.  (SCI,IF 2.791)

    • Jimeng Li, Xifeng   Yao, Hui Wang ,et al. Periodic impulses extraction based on improved adaptive VMD and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis[J]. Mechanical  Systems and Signal Processing, 2019, 568-589. (SCI 中科院二区 Top)

    • Jimeng Li, Jinfeng Zhang, Ming Li, et al. A novel adaptive stochastic resonance method based on coupled bistable systems and its application in rolling bearing fault diagnosis[J]. Mechanical Systems and Signal Processing, 2019, 128-145. (SCI 中科院二区 Top)

    • Jimeng Li, Hui  Wang, Jinfeng Zhang, et al. Impact fault detection of gearbox based on variational mode decomposition and coupled underdamped stochastic resonance[J]. ISA Transactions, 2019. (SCI 中科院二区)

    •  Jimeng Li, Ming  Li, Jinfeng Zhang, et al. Frequency-shift multiscale noise tuning stochastic resonance method for fault diagnosis of generator bearing in wind   turbine[J]. Measurement,  2019. 421-432. (SCI,IF 2.791)

    •  Jimeng Li, Ming  Li, Xifeng Yao, et al. An adaptive randomized orthogonal   matching pursuit algorithm with sliding window for rolling bearing fault   diagnosis[J]. IEEE Access, 2018. (SCI,中科院二区)

    • Jimeng Li, Ming Li, Jinfeng Zhang. Rolling bearing fault diagnosis based on time-delayed feedback monostable stochastic resonance and adaptive minimum entropy deconvolution[J]. Journal of Sound and Vibration, 2017: 139-151. (SCI,IF 3.123)

    • Jimeng Li, Yungang Zhang, Ping Xie. A new adaptive cascaded stochastic resonance method for impact features extraction in gear fault diagnosis. Measurement, 2016, 91: 499-508. (SCI,IF   2.791)

    • Jimeng Li,   Jinfeng Zhang. Adaptive multiscale noise control enhanced stochastic resonance method based on modified eemd with its application in bearing fault diagnosis. Shock and Vibration, 2016: 1485412_1:13. (SCI,IF 1.62)

    • Jimeng Li, Xuefeng Chen, Zhengjia He. Multi-stable stochastic resonance and its application research on mechanical fault diagnosis. Journal of Sound and Vibration, 2013, 22: 5999-6015. (SCI,IF 3.123)

    • Jimeng Li, Xuefeng Chen, Zhengjia He. Adaptive stochastic resonance method for impact signal detection based on sliding window. Mechanical Systems and Signal Processing, 2013, 36: 240-255. (SCI 中科院二区 Top)

    • Jimeng Li, Xuefeng Chen, Zhaohui Du, et al. A New noise-controlled second-order enhanced stochastic resonance method with   its application in wind turbine drivetrain fault diagnosis. Renewable Energy, 2013, 60: 7-19. (SCI 中科院二区)

    • Guoying Li, Jimeng Li, Shibin Wang, et al. Quantitative evaluation on the performance and feature enhancement of stochastic resonance for bearing fault diagnosis. Mechanical System and Signal Processing, 2016, 2016, 81:  108-125. (SCI 中科院二区)

    • 李继猛,李铭,王慧,等. 基于相关正交匹配追踪算法的风电机组滚动轴承稀疏故障诊断方法. 中国机械工程,2018.

    • 李继猛,张云刚,张金凤,等. 基于自适应随机共振的齿轮微弱冲击故障增强提取方法研究. 计量学报, 2017. 

    • 李继猛,黄梦君,谢平,等. 同步压缩-交叉小波变换及滚动轴承故障特征增强. 计量学报, 2017. 

    • 李继猛,张金凤,张云刚,等. 基于自适应随机共振和稀疏编码收缩算法的齿轮故障诊断方法. 中国机械工程,2016.  

论文成果

More>>

专利

科研项目

个人简介

李继猛,博士,副教授,硕士生导师,2013年毕业于西安交通大学,获博士学位,主要从事机械系统动力学建模和故障机理分析、机械系统动态信号处理和微弱故障诊断、风电装备运行状态监测和故障诊断、大数据分析与智能运维等方面研究,独立承担国家自然科学基金项目1项、河北省自然科学基金项目1项、中国博士后科学基金项目1项及市级科研项目1项,已发表EI/SCI检索学术论文20余篇,申请或授权发明专利3项。

其他联系方式

邮编 :

  • 传真 :

  • 通讯/办公地址 :

  • 办公室电话 :

  • 移动电话 :

  • 邮箱 :

  • 扫一扫用手机查看