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D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
76
Citations
33163
World Ranking
262
National Ranking
34

Overview

Yaguo Lei is affiliated with Xi'an Jiaotong University in China and focuses on research within the field of engineering, specifically emphasizing control and systems engineering, mechanical engineering, mechanics of materials, civil and structural engineering, and safety, risk, reliability, and quality.

Their research primarily covers the following main topics:

  • Machine Fault Diagnosis Techniques
  • Fault Detection and Control Systems
  • Gear and Bearing Dynamics Analysis
  • Reliability and Maintenance Optimization
  • Non-Destructive Testing Techniques
  • Engineering Diagnostics and Reliability
  • Tribology and Lubrication Engineering

Yaguo Lei frequently publishes in a number of specialized venues, with notable contributions to:

  • Mechanical Systems and Signal Processing
  • Reliability Engineering & System Safety
  • IEEE Transactions on Industrial Electronics
  • IEEE/CAA Journal of Automatica Sinica
  • SSRN Electronic Journal

Among recent papers authored or co-authored by Yaguo Lei are:

  • Applications of machine learning to machine fault diagnosis: A review and roadmap (2020), Mechanical Systems and Signal Processing
  • Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery (2020), IEEE Transactions on Industrial Electronics
  • Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution (2021), Mechanical Systems and Signal Processing
  • Distribution-Invariant Deep Belief Network for Intelligent Fault Diagnosis of Machines Under New Working Conditions (2020), IEEE Transactions on Industrial Electronics
  • Intelligent Machinery Fault Diagnosis With Event-Based Camera (2023), IEEE Transactions on Industrial Informatics

Collaborative research with frequent co-authors includes contributions with:

  • Naipeng Li
  • Xiang Li
  • Bin Yang
  • Junyi Cao
  • Wei-Hsin Liao

Best Publications

  • Applications of machine learning to machine fault diagnosis: A review and roadmap

    Yaguo Lei;Bin Yang;Xinwei Jiang;Feng Jia

  • Machinery health prognostics: A systematic review from data acquisition to RUL prediction

    Yaguo Lei;Naipeng Li;Liang Guo;Ningbo Li

  • A review on empirical mode decomposition in fault diagnosis of rotating machinery

    Yaguo Lei;Jing Lin;Zhengjia He;Ming J. Zuo

  • Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

    Feng Jia;Yaguo Lei;Jing Lin;Xin Zhou

  • A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

    Biao Wang;Yaguo Lei;Naipeng Li;Ningbo Li

  • An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

    Yaguo Lei;Feng Jia;Jing Lin;Saibo Xing

  • A recurrent neural network based health indicator for remaining useful life prediction of bearings

    Liang Guo;Naipeng Li;Feng Jia;Yaguo Lei

  • An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings

    Bin Yang;Yaguo Lei;Feng Jia;Saibo Xing

  • Condition monitoring and fault diagnosis of planetary gearboxes: A review

    Yaguo Lei;Jing Lin;Ming J. Zuo;Ming J. Zuo;Zhengjia He

  • Application of the EEMD method to rotor fault diagnosis of rotating machinery

    Yaguo Lei;Zhengjia He;Yanyang Zi

  • A Model-Based Method for Remaining Useful Life Prediction of Machinery

    Yaguo Lei;Naipeng Li;Szymon Gontarz;Jing Lin

  • Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs

    Yaguo Lei;Zhengjia He;Yanyang Zi;Qiao Hu

  • Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization

    Feng Jia;Yaguo Lei;Na Lu;Saibo Xing

  • Application of an improved kurtogram method for fault diagnosis of rolling element bearings

    Yaguo Lei;Jing Lin;Zhengjia He;Yanyang Zi

  • A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines

    Feng Jia;Yaguo Lei;Liang Guo;Jing Lin

  • A new approach to intelligent fault diagnosis of rotating machinery

    Yaguo Lei;Zhengjia He;Yanyang Zi

  • Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings

    Yonghao Miao;Ming Zhao;Ming Zhao;Jing Lin;Yaguo Lei

  • Deep separable convolutional network for remaining useful life prediction of machinery

    Biao Wang;Yaguo Lei;Naipeng Li;Tao Yan

  • EEMD method and WNN for fault diagnosis of locomotive roller bearings

    Yaguo Lei;Zhengjia He;Yanyang Zi

  • Application of an intelligent classification method to mechanical fault diagnosis

    Yaguo Lei;Zhengjia He;Yanyang Zi

  • Planetary gearbox fault diagnosis using an adaptive stochastic resonance method

    Yaguo Lei;Yaguo Lei;Dong Han;Jing Lin;Zhengjia He

  • Gear crack level identification based on weighted K nearest neighbor classification algorithm

    Yaguo Lei;Ming J. Zuo

  • New clustering algorithm-based fault diagnosis using compensation distance evaluation technique

    Yaguo Lei;Zhengjia He;Yanyang Zi;Xuefeng Chen

  • A multidimensional hybrid intelligent method for gear fault diagnosis

    Yaguo Lei;Ming J. Zuo;Zhengjia He;Yanyang Zi

Frequent Co-Authors

Jing Lin
Jing Lin Beihang University
Zhengjia He
Zhengjia He Xi'an Jiaotong University
Yanyang Zi
Yanyang Zi Xi'an Jiaotong University
Ming J. Zuo
Ming J. Zuo University of Alberta
Ming Zhao
Ming Zhao Xi'an Jiaotong University
Steven X. Ding
Steven X. Ding University of Duisburg-Essen
Qinghua Hu
Qinghua Hu Tianjin University
Junyi Cao
Junyi Cao Xi'an Jiaotong University
Asoke K. Nandi
Asoke K. Nandi Brunel University London
Linkan Bian
Linkan Bian Mississippi State University

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