World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
47
Citations
6781
World Ranking
3286
National Ranking
532

Overview

Qingbo He is affiliated with Shanghai Jiao Tong University in China and has a substantial research portfolio in the field of engineering, with a focus on control and systems engineering as well as mechanical and civil engineering disciplines.

The primary fields of study include:

  • Engineering

Subfields where Qingbo He has contributed significantly are:

  • Control and Systems Engineering
  • Mechanical Engineering
  • Civil and Structural Engineering
  • Electrical and Electronic Engineering
  • Mechanics of Materials

The research topics covered in their work are:

  • Machine Fault Diagnosis Techniques
  • Fault Detection and Control Systems
  • Structural Health Monitoring Techniques
  • Gear and Bearing Dynamics Analysis
  • Acoustic Wave Phenomena Research
  • Speech and Audio Processing
  • Ultrasonics and Acoustic Wave Propagation

Qingbo He has coauthored work with several frequent collaborators, including:

  • Zhike Peng
  • Fengshou Gu
  • Tianxi Jiang
  • Xiaoxi Ding
  • Junchao Guo

The scientist's research has been published commonly in the following venues:

  • IEEE Transactions on Instrumentation and Measurement
  • Mechanical Systems and Signal Processing
  • IEEE Transactions on Industrial Electronics
  • IEEE Transactions on Industrial Informatics
  • Journal of Sound and Vibration

Representative recent papers include:

  • Edge Computing on IoT for Machine Signal Processing and Fault Diagnosis: A Review, 2023, IEEE Internet of Things Journal
  • Rotating machinery fault-induced vibration signal modulation effects: A review with mechanisms, extraction methods and applications for diagnosis, 2023, Mechanical Systems and Signal Processing
  • Effectiveness of PEMFC historical state and operating mode in PEMFC prognosis, 2020, International Journal of Hydrogen Energy
  • Randomized resonant metamaterials for single-sensor identification of elastic vibrations, 2020, Nature Communications
  • Manifold Sensing-Based Convolution Sparse Self-Learning for Defective Bearing Morphological Feature Extraction, 2020, IEEE Transactions on Industrial Informatics

Best Publications

  • Energy-Fluctuated Multiscale Feature Learning With Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis

    Xiaoxi Ding;Qingbo He

  • A review of stochastic resonance in rotating machine fault detection

    Siliang Lu;Qingbo He;Jun Wang

  • Fault diagnosis of rotating machines based on the EMD manifold

    Jun Wang;Guifu Du;Zhongkui Zhu;Changqing Shen

  • Subspace-based gearbox condition monitoring by kernel principal component analysis

    Qingbo He;Fanrang Kong;Ruqiang Yan

  • Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines

    Qingbo He;Jun Wang;Yongbin Liu;Daoyi Dai

  • Effects of multiscale noise tuning on stochastic resonance for weak signal detection

    Qingbo He;Jun Wang

  • Machine condition monitoring using principal component representations

    Qingbo He;Ruqiang Yan;Fanrang Kong;Ruxu Du

  • Vibration signal classification by wavelet packet energy flow manifold learning

    Qingbo He

  • Effects of underdamped step-varying second-order stochastic resonance for weak signal detection

    Siliang Lu;Qingbo He;Fanrang Kong

  • Stochastic resonance with Woods-Saxon potential for rolling element bearing fault diagnosis

    Siliang Lu;Qingbo He;Fanrang Kong

  • Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals

    Siliang Lu;Xiaoxian Wang;Qingbo He;Fang Liu

  • Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement

    Qingbo He;Jun Wang;Fei Hu;Fanrang Kong

  • Adaptive Multiscale Noise Tuning Stochastic Resonance for Health Diagnosis of Rolling Element Bearings

    Jun Wang;Qingbo He;Fanrang Kong

  • Structure damage localization with ultrasonic guided waves based on a time-frequency method

    Daoyi Dai;Qingbo He

  • A fusion feature and its improvement based on locality preserving projections for rolling element bearing fault classification

    Xiaoxi Ding;Qingbo He;Nianwu Luo

  • Time-Frequency Manifold as a Signature for Machine Health Diagnosis

    Qingbo He;Yongbin Liu;Qian Long;Jun Wang

  • Online Fault Diagnosis of Motor Bearing via Stochastic-Resonance-Based Adaptive Filter in an Embedded System

    Siliang Lu;Qingbo He;Tao Yuan;Fanrang Kong

  • Sequential Multiscale Noise Tuning Stochastic Resonance for Train Bearing Fault Diagnosis in an Embedded System

    Siliang Lu;Qingbo He;Fei Hu;Fanrang Kong

  • Bearing fault diagnosis of a permanent magnet synchronous motor via a fast and online order analysis method in an embedded system

    Siliang Lu;Qingbo He;Jiwen Zhao

  • Time–frequency manifold for nonlinear feature extraction in machinery fault diagnosis

    Qingbo He

  • Rotating machine fault diagnosis through enhanced stochastic resonance by full-wave signal construction

    Siliang Lu;Qingbo He;Haibin Zhang;Fanrang Kong

Frequent Co-Authors

Siliang Lu
Siliang Lu Anhui University
Changqing Shen
Changqing Shen Soochow University
Andrew Ball
Andrew Ball University of Huddersfield
Fengshou Gu
Fengshou Gu University of Huddersfield

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