Achievements

奖励与荣誉

  • 国家技术发明二等奖
  • 中国青年科技奖
  • 科睿唯安全球高被引科学家（工程学、跨学科领域）
  • 教育部自然科学一等奖
  • 教育部青年科学奖
  • 爱思唯尔中国高被引学者（机械工程、计算机科学）


学术专著
  • Yaguo Lei, Naipeng Li, Xiang Li, Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems [M]. Springer, 2022.
  • 雷亚国,杨彬. 大数据驱动的机械装备智能运维理论及应用 [M]. 电子工业出版社, 2022.
  • Yaguo Lei, Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery [M]. Elsevier Butterworth-Heinemann, Oxford, 2016.

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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.
  • Xiao Yang, Yaguo Lei, Huan Liu, Bin Yang, Xiang Li, Naipeng Li, Rigid-flexible modeling of compound multistage gear system considering flexibility of shaft and gear elastic deformation [J]. Mechanical Systems and Signal Processing, 2023, 200: 110632.
  • Huan Liu, Yaguo Lei, Xiao Yang, Wenlei Song, Junyi Cao, Deflection estimation of industrial robots with flexible joints [J]. Fundamental Research, 2021, in press.
  • 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.
  • 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.
  • 雷亚国,罗希,刘宗尧,卢帆勃,林京,汤伟. 行星轮系动力学新模型及其故障响应特性研究[J]. 机械工程学报, 2016, 52(13): 111-122.
  • 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.
  • Xiwei Li, Yaguo Lei, Mingzhong Xu, Naipeng Li, Dengke Qiang, Qubing Ren, Xiang Li, A spectral self-focusing fault diagnosis method for automotive transmissions under gear-shifting conditions [J]. Mechanical Systems and Signal Processing, 2023, 200: 110499.
  • 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.
  • 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.
  • 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.
  • 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 (Invited Review Paper) [J]. Mechanical Systems and Signal Processing, 2020, 138: 106587.
  • Bin Yang, Yaguo Lei, Xiang Li, Clive Roberts, Deep targeted transfer learning along designable adaptation trajectory for fault diagnosis across different machines [J]. IEEE Transactions on Industrial Electronics, 2023, 70(9): 9463 - 9473.
  • 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, Xuefeng Chen, New clustering algorithm based fault diagnosis using compensation distance evaluation technique [J]. Mechanical Systems and Signal Processing, 2008, 22(2): 419-435.
  • 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.
  • 雷亚国,杨彬,李乃鹏,李响,武通海. 跨设备的机械故障靶向迁移诊断方法[J]. 机械工程学报, 2022, 58(12): 1-9.
  • 雷亚国,许学方,蔡潇,李乃鹏,孔德同,张勇铭. 面向机械装备健康监测的数据质量保障方法研究[J]. 机械工程学报, 2021, 57(4): 1-9.
  • 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(7): 1-8.

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.
  • Naipeng Li, Yaguo Lei, Xiaofei Liu, Tao Yan, Pengcheng Xu, Machinery health prognostics with multi-model fusion degradation modeling [J]. IEEE Transactions on Industrial Electronics, 2023, 70(11): 11764 – 11773.
  • Yuan Wang, Yaguo Lei, Naipeng Li, Tao Yan, Xiaosheng Si, Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery [J]. Reliability Engineering & System Safety, 2023, 231: 109006.
  • Tao Yan, Yaguo Lei, Naipeng Li, Biao Wang, Wenting Wang, Degradation modeling and remaining useful life prediction for dependent competing failure processes [J]. Reliability Engineering & System Safety, 2021, 212: 107638.
  • 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.
  • 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.
  • 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.
  • 李乃鹏,蔡潇,雷亚国,徐鹏程,王文廷,王彪. 一种融合多传感器数据的数模联动机械剩余寿命预测方法[J]. 机械工程学报, 2021, 57(20): 29-37.

机械装备健康维护决策
  • Tao Yan, Yaguo Lei, Naipeng Li, Liliane Pintelon, Reginald Dewil, Joint optimization of maintenance and spare parts inventory for multi-unit systems with a generalized structure [J]. Transactions of the ASME, Journal of Manufacturing Science and Engineering, 2022, in press.
  • Tao Yan, Yaguo Lei, Naipeng Li, Xiaosheng Si, Liliane Pintelon, Reginald Dewil, Online joint replacement-order optimization driven by a nonlinear ensemble remaining useful life prediction method [J]. Mechanical Systems and Signal Processing, 2022, 173:109053.
  • Tao Yan, Yaguo Lei, Biao Wang, Tianyu Han, Xiaosheng Si, Naipeng Li, Joint maintenance and spare parts inventory optimization for multi-unit systems considering imperfect maintenance actions [J]. Reliability Engineering & System Safety, 2020, 202: 106994.