奖励与成果

奖励与荣誉

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  • 国家技术发明二等奖
  • 中国青年科技奖
  • 国家杰出青年科学基金获得者
  • 教育部自然科学一等奖
  • 教育部青年科学奖
  • 国家高层次人才特殊支持计划入选者
  • 陕西青年五四奖章
  • 德国洪堡学者
  • 科睿唯安全球高被引科学家​​​​​​​


英文专著

  • Yaguo Lei, Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery [M]. Elsevier Butterworth-Heinemann, Oxford, 2016.​​​​​​​

 

代表性论文

机械系统动态建模

  • YaguoLei, Jing Lin, Ming J. Zuo, Zhengjia He, Condition monitoring and fault diagnosis of planetary gearboxes: A review [J]. Measurement, 2014, 48(2): 292-305.
  • Yaguo Lei, Zongyao Liu, Jing Lin, Fanbo Lu, Phenomenological models of vibration signals for condition monitoring and fault diagnosis of epicyclic gearboxes [J]. Journal of Sound and Vibration, 2016, 369: 266-281.
  • Zongyao Liu, Yaguo Lei, Huan Liu, Xiao Yang, Wenlei Song, A phenomenological model for investigating unequal planet load sharing in epicyclic gearboxes [J]. Mechanical Systems and Signal Processing, 2020, 135: 106414.
  • 雷亚国,汤伟,孔德同,林京. 基于传动机理分析的行星齿轮箱振动信号仿真及其故障诊断[J]. 机械工程学报, 2014, 50(5): 17-24.

机械信号处理与分析

  • Zijian Qiao, Yaguo Lei, Naipeng Li, Applications of stochastic resonance to machinery fault detection: A review and tutorial [J]. Mechanical Systems and Signal Processing, 2019, 122:502-536.
  • Xuefang Xu, Zijian Qiao, Yaguo Lei, Repetitive transient extraction for machinery fault diagnosis using multiscale fractional order entropy infogram [J]. Mechanical Systems and Signal Processing, 2018, 103: 312-326.
  • Zijian Qiao, Yaguo Lei, Jing Lin, Shantao Niu, Stochastic resonance subject to multiplicative and additive noise: The influence of potential asymmetries [J]. Physical Review E, 2016, 94(5): 052214-1-13.
  • Zijian Qiao, Yaguo Lei, Jing Lin, Feng Jia, An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis [J]. Mechanical Systems and Signal Processing, 2017, 84: 731-746.
  • Yaguo Lei, Jing Lin, Zhengjia He, Ming J. Zuo, A review on empirical mode decomposition in fault diagnosis of rotating machinery [J]. Mechanical Systems and Signal Processing, 2013, 35(1-2): 108-126.
  • Yaguo Lei, Zhengjia He, Yanyang Zi, Application of the EEMD method to rotor fault diagnosis of rotating machinery [J]. Mechanical Systems and Signal Processing, 2009, 23(4): 1327-1338.

大数据下智能故障诊断

  • Yaguo Lei, Bin Yang, Xinwei Jiang, Feng Jia, Naipeng Li, Asoke K. Nandi, Applications of machine learning to machine fault diagnosis: A review and roadmap [J]. Mechanical Systems and Signal Processing, 2020, 138: 106587.
  • Bin Yang, Yaguo Lei, Feng Jia, Saibo Xing, An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings [J]. Mechanical Systems and Signal Processing, 2019,122:692-706.
  • Liang Guo, Yaguo Lei, Saibo Xing, Tao Yan, Naipeng Li, Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data [J]. IEEE Transactions on Industrial Electronics, 2019, 66(9): 7316-7325.
  • Feng Jia, Yaguo Lei, Jing Lin, Xin Zhou, Na Lu, Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data [J]. Mechanical Systems and Signal Processing, 2016, 72-73: 303-315.
  • Yaguo Lei, Zhengjia He, Yanyang Zi, and Qiao Hu, Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs [J]. Mechanical Systems and Signal Processing, 2007, 21(5): 2280-2294.
  • Yaguo Lei, Zhengjia He, Yanyang Zi, Xuefeng Chen, New clustering algorithm based fault diagnosis using compensation distance evaluation technique [J]. Mechanical Systems and Signal Processing, 2008, 22(2): 419-435.
  • 雷亚国,杨彬,杜兆钧,吕娜. 大数据下机械装备故障的深度迁移诊断方法[J]. 机械工程学报, 2019, 55(07): 1-8.
  • Bin Yang, Yaguo Lei, Feng Jia, Naipeng Li, Zhaojun Du, A polynomial kernel induced distance metric to improve deep transfer learning for fault diagnosis of machines [J]. IEEE Transactions on Industrial Electronics, 2020, 67(11): 9747-9757.
  • Xuefang Xu, Yaguo Lei, Zeda Li, An incorrect data detection method for big data cleaning of machinery condition monitoring [J]. IEEE Transactions on Industrial Electronics, 2020, 67(3): 2326-2336.

机械装备剩余寿命预测

  • Yaguo Lei, Naipeng Li, Liang Guo, Ningbo Li, Tao Yan, Jing Lin, Machinery health prognostics: A systematic review from data acquisition to RUL prediction [J]. Mechanical Systems and Signal Processing, 2018, 104: 799-834.
  • Biao Wang, Yaguo Lei, Naipeng Li, Ningbo Li, A hybrid prognostics approach for estimating remaining useful life of rolling element bearings [J]. IEEE Transactions on Reliability, 2020, 69(1):401-412.
  • Naipeng Li, Yaguo Lei, Tao Yan, Ningbo Li, Tianyu Han, A wiener process model-based method for remaining useful life prediction considering unit-to-unit variability [J]. IEEE Transactions on Industrial Electronics, 2019, 66(3): 2092-2101.
  • Yaguo Lei, Naipeng Li, Szymon Gontarz, Jing Lin, Stanislaw Radkowski, Jacek Dybala, A model-based method for remaining useful life prediction of machinery [J]. IEEE Transactions on Reliability, 2016, 65(3): 1314-1326.
  • Naipeng Li, Yaguo Lei, Jing Lin, Steven X. Ding, An improved exponential model for predicting remaining useful life of rolling element bearings [J]. IEEE Transactions on Industrial Electronics, 2015, 62(12): 7762-7773.
  • Naipeng Li, Yaguo Lei, Liang Guo, Tao Yan, Jing Lin, Remaining useful life prediction based on a general expression of stochastic process models [J]. IEEE Transactions on Industrial Electronics, 2017, 64(7): 5709-5718.
  • Biao Wang, Yaguo Lei, Naipeng Li, Tao Yan, Deep separable convolutional network for remaining useful life prediction of machinery [J]. Mechanical Systems and Signal Processing, 2019, 134: 106330.
  • Naipeng Li, Nagi Gebraeel, Yaguo Lei, Linkan Bian, Xiaosheng Si, Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model [J]. Reliability Engineering & System Safety, 2019, 186:88-100.​​​​​​​