Prof. Yaguo Lei

Email: yaguolei@mail.xjtu.edu.cn

Tel: (+86) 029-83395041

School of Mechanical Engineering

Xi’an Jiaotong University

No. 28 Xianning West Road

710049 Xi’an, P.R.China

 

Biography

Prof. Yaguo Lei received the B.E. degree and the Ph.D. degree both in mechanical engineering from Xi’an Jiaotong University, P. R. China, in 2002 and 2007, respectively. He is currently a professor in mechanical engineering of Xi’an Jiaotong University, P. R. China. He ever worked at the University of Duisburg-Essen, Germany as an Alexander von Humboldt fellow and at the University of Alberta, Canada as a postdoctoral research fellow. He is also a fellow of IET and ISEAM, a senior member of IEEE, a member of ASME, and senior members of CMES, ORSC and CAA, respectively, and the associate editors/ the editorial board members of IEEE TIE, MSSP, NC&A, MST, etc. His research interests include health condition monitoring and intelligent maintenance, big-data era intelligent fault diagnostics and prognostics, reliability evaluation and remaining useful life prediction, mechanical signal analysis and processing, and mechanical system dynamic modeling. He has pioneered many signal processing techniques and intelligent models for diagnosing mechanical faults such as gearboxes, bearings, rotor systems etc. He has published 80 peer-reviewed papers on signal processing and fault diagnostics, which have been broadly cited by hundreds of researchers in this field.​​​​​​​

Honors & Awards

Fellow of IET

Fellow of ISEAM

Fellow of Alexander von Humboldt

Highly Cited Researcher by Clarivate (2019, 2020)

Outstanding Young Scholars of NSFC

Youth Science Award of MOE

China Youth Science and Technology Award

Young Researcher New Star Scientist of CAS

Counter

Research Group

Education & Employment

EMPLOYMENT HISTORY

  • 2013 - present

Full Professor, School of Mechanical Engineering, Xi’an Jiaotong University, P.R. China
  • 2012 - 2013

AvH Fellow, Automatic Control and Complex Systems, University of Duisburg-Essen, Germany
  • 2010 - 2013

Associate Professor, School of Mechanical Engineering, Xi’an Jiaotong University, P.R. China
  • 2008 - 2009

Postdoctoral Fellow, Department of Mechanical Engineering, University of Alberta, Canada

EDUCATION
  • 2002 - 2007

Ph.D., Mechanical Engineering, Xi’an Jiaotong University, P.R. China
  • 1998 - 2002

B.E., Mechanical Engineering and Automation, Xi’an Jiaotong University, P.R. China​​

Scientific Research

RESEARCH INTERESTS
  • Health condition monitoring and intelligent maintenance
  • Big-data era intelligent fault diagnostics and prognostics
  • Reliability evaluation and remaining useful life prediction
  • Mechanical signal analysis and processing
  • Mechanical system dynamic modeling

RESEARCH MONOGRAPHS
  • Yaguo Lei, Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery. Elsevier Butterworth-Heinemann, Oxford, 2016.

SELECTED PUBLICATIONS
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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. Mechanical Systems and Signal Processing, 2017, 84: 731-746 .
  • Yaguo Lei, Naipeng Li, Szymon Gontarz, Jing Lin, Stanislaw Radkowski, Jacek Dybala, A model-based method for remaining useful life prediction of machinery. IEEE Transactions on Reliability, 2016, 65(3): 1314-1326.
  • 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. Mechanical Systems and Signal Processing, 2016, 72-73: 303-315.
  • Naipeng Li, Yaguo Lei, Jing Lin, Steven X. Ding, An improved exponential model for predicting remaining useful life of rolling element bearings. IEEE Transactions on Industrial Electronics, 2015, 62(12): 7762-7773.
  • YaguoLei, Jing Lin, Ming J. Zuo, Zhengjia He, Condition monitoring and fault diagnosis of planetary gearboxes: A review. Measurement, 2014, 48(2): 292-305.
  • Yaguo Lei, Jing Lin, Zhengjia He, Ming J. Zuo, A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 2013, 35(1-2): 108-126.
  • Yaguo Lei, Dong Han, Jing Lin, Zhengjia He, Planetary gearbox fault diagnosis using an adaptive stochastic resonance method. Mechanical Systems and Signal Processing, 2013, 38(1): 113-124.
  • Yaguo Lei, Zhengjia He, Yanyang Zi, Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 2009, 23(4): 1327-1338.
  • Yaguo Lei, Ming J Zuo, Gear crack level identification based on weighted K nearest neighbor classification algorithm. Mechanical Systems and Signal Processing, 2009, 23(5): 1535-1547.
  • Yaguo Lei, Zhengjia He, Yanyang Zi, Xuefeng Chen, New clustering algorithm based fault diagnosis using compensation distance evaluation technique. Mechanical Systems and Signal Processing, 2008, 22(2): 419-435.
  • Yaguo Lei, Zhengjia He, Yanyang Zi, Qiao Hu, Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mechanical Systems and Signal Processing, 2007, 21(5): 2280-2294.
  • 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.
  • 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.
  • 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.
  • 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.