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.S. 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, 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 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

² Fellowship of IET

² Alexander von Humboldt Fellow

² Excellent Young Scientists of NSFC

² Young Science Award of MOE

² Young Cheung Kong Scholars of MOE

² National Program for Support of Top-notch Young Professionals

² New Century Excellent Talents of MOE

² Young Researcher New Star Scientist of CAS

Counter
Research Group

Education & Employment

EMPLOYMENT HISTORY

² 2013 - present

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

² 2012 - 2013

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

² 2010 - 2013

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

² 2008 - 2009

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

EDUCATION

² 2002 - 2007

Ph.D., Mechanical Engineering, Xi’an Jiaotong University, P.R. China

² 1998 - 2002

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

Scientific Research

RESEARCH INTERESTS

² Health condition monitoring and intelligent maintenance

² Big-data era intelligent fault diagnostics and prognostics

² Reliability evaluation and remaining useful life prediction

² Mechanical signal analysis and processing

² Mechanical system dynamic modeling

RESEARCH MONOGRAPHS

² Yaguo Lei, Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery. Elsevier Butterworth-Heinemann, Oxford, 2016.

² Mohammad Fazle Azeem, Fuzzy Inference System–Theory and Applications. InTech, Croatia, 2012. (Composed one chapter)

SELECTED PUBLICATIONS

² 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, 2019, Online.

² 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.

² 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, 2019, in press.

² 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.

² 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.

² 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, 2019, in press.

² 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, in press.

² 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.

² Feng Jia, Yaguo Lei, Na Lu, Saibo Xing, Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization [J]. Mechanical Systems and Signal Processing, 2018, 110: 349-367.

² Liang Guo, Yaguo Lei, Naipeng Li, Tao Yan, Ningbo Li, Machinery health indicator construction based on convolutional neural networks considering trend burr [J]. Neurocomputing, 2018, 292: 142-150.

² Feng Jia, Yaguo Lei, Liang Guo, Jing Lin, Saibo Xing, A neural network constructed by deep  learning technique and its application to intelligent fault diagnosis of machines [J]. Neurocomputing, 2018, 272: 619-628.

² Yaguo Lei, Zongyao Liu, Delong Wang, Xiao Yang, Huan Liu, Jing Lin, A probability distribution model of tooth pits for evaluating time-varying mesh stiffness of pitting gears [J]. Mechanical Systems and Signal Processing, 2018, 106:355-366.

² 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.

² Feng Jia, Yaguo Lei, Liang Guo, Jing Lin, Saibo Xing, A new network constructed by normalized sparse autoencoder and its application to intelligent fault diagnosis of machines [J]. Neurocomputing, 2018, 272: 619-628.

² 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.

² Liang Guo, Naipeng Li, Feng Jia, Yaguo Lei, Jing Lin, A recurrent neural network based health indicator for remaining useful life prediction of bearings [J]. Neurocomputing, 2017, 240: 98-109.

² Yaguo Lei, Zijian Qiao, Xuefang Xu, Jing Lin, Shantao Niu, An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings [J]. Mechanical Systems and Signal Processing, 2017, 94: 148-164.

² Yaguo Lei, Naipeng Li, Jing Lin, A new method based on stochastic process models for machine remaining useful life prediction [J]. IEEE Transactions on Instrumentation and Measurement, 2016, 65(12): 2671-2684.

² 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.

² Yaguo Lei, Zongyao Liu, Jing Lin, Fanbo Lu, Phenomenological models of vibration signals for condition monitoring and fault diagnosis of epicyclic gearboxes. Journal of Sound and Vibration, 2016, 369: 266-281.

² 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 .

² 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.

² Yaguo Lei, Feng Jia, Jing Lin, Saibo Xing, Steven X. Ding, An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Transactions on Industrial Electronics, 2016, 63(5): 3137-3147.

² 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.

² 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.

² Yaguo Lei, Zongyao Liu, Xionghui Wu, Naipeng Li, Wu Chen, Jing Lin, Health condition identification of multi-stage planetary gearboxes using a mRVM-based method. Mechanical Systems and Signal Processing, 2015, 60-61: 289-300.

² YaguoLei, Jing Lin, Ming J. Zuo, Zhengjia He, Condition monitoring and fault diagnosis of planetary gearboxes: A review. Measurement, 2014, 48(2): 292-305.

² YaguoLei, JingLin, Dong Han, Zhengjia He, An enhanced stochastic resonance method for weak feature extraction from vibration signals in bearing fault detection. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2014, 228(5): 815-827.

² 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.

² 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.

² YaguoLei, Naipeng Li, Jing Lin, Sizhe Wang, Fault diagnosis of rotating machinery based on an adaptive ensemble empirical mode decomposition. Sensors, 2013, 13(12): 16950-16964.

² Yaguo Lei, Detong Kong, Jing Lin, Ming J. Zuo, Fault detection of planetary gearboxes using new diagnostic parameters. Measurement Science & Technology, 2012, 23(5): 1-10.

² Yaguo Lei, JingLin, Zhengjia He, Detong Kong, A method based on multi-sensor data fusion for fault detection of planetary gearboxes. Sensors, 2012, 12(2): 2005-2017.

² Yaguo Lei, Jing Lin, Zhengjia He, Yanyang Zi, Application of an improved kurtogram method for fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing, 2011, 25(5): 1738-1749.

² Yaguo Lei, Zhengjia He, Yanyang Zi, EEMD method and WNN for fault diagnosis of locomotive roller bearings. Expert Systems with Applications, 2011, 38(6): 7334-7341.

² Yaguo Lei, Ming J Zuo, Mohammad R Hoseini, The use of ensemble empirical mode decomposition to improve bispectral analysis for fault detection in rotating machinery. Proceedings of the Institution of Mechanical Engineers, Part C, Journal of Mechanical Engineering Science, 2010, 224(8): 1759-1769.

² Yaguo Lei, Ming J Zuo, Zhengjia He, Yanyang Zi, A multidimensional hybrid intelligent method for gear fault diagnosis. Expert Systems with Applications, 2010, 37(2): 1419-1430.

² 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.

² Yaguo Lei, Ming J Zuo, Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs. Measurement Science & Technology, 2009, 20(12): 1-12.

² Yaguo Lei, Zhengjia He, Yanyang Zi, A combination of WKNN to fault diagnosis of rolling element bearings. Transactions of the ASME, Journal of Vibration and Acoustics, 2009, 131(6): 1-6.

² 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.

² Yaguo Lei, Zhengjia He, YanyangZi, HuaxinChen, A new approach to intelligent fault diagnosis of rotating machinery. Expert Systems with Applications, 2008, 35(4): 1593-1600.

² 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.

² Yaguo Lei, Zhengjia He, Yanyang Zi, Qiao Hu, Fault Diagnosis of rotating machinery based on a new hybrid clustering algorithm. International Journal of Advanced Manufacturing Technology, 2008, 35(9-10): 968-977.

² 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.