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Zhongkui Zhu

Zhongkui Zhu

D-Index & Metrics

Engineering and Technology

D-Index
44
Citations
6102
World Ranking
5935
National Ranking
1138

Overview

Zhongkui Zhu is affiliated with Soochow University in China and has a prominent research profile in engineering. Their work is focused primarily on areas including control and systems engineering, mechanical engineering, mechanics of materials, artificial intelligence, and electrical and electronic engineering.

The research topics central to Zhu's publications encompass machine fault diagnosis techniques, gear and bearing dynamics analysis, fault detection and control systems, engineering diagnostics and reliability, structural health monitoring techniques, railway engineering and dynamics, and anomaly detection techniques and applications.

Zhu has contributed extensively to several academic journals and conferences. The most frequent venues for their publications include:

  • IEEE Transactions on Instrumentation and Measurement
  • Measurement Science and Technology
  • IEEE Sensors Journal
  • Mechanical Systems and Signal Processing
  • Advanced Engineering Informatics

Significant recent papers authored by Zhu cover a range of diagnostic technologies and methodologies for machinery, showing a strong focus on fault diagnosis and advanced signal processing methods. These papers include:

  • Multi-scale deep intra-class transfer learning for bearing fault diagnosis, 2020, published in Reliability Engineering & System Safety
  • Bearing fault diagnosis via generalized logarithm sparse regularization, 2021, published in Mechanical Systems and Signal Processing
  • Knowledge mapping-based adversarial domain adaptation: A novel fault diagnosis method with high generalizability under variable working conditions, 2020, published in Mechanical Systems and Signal Processing
  • Central frequency mode decomposition and its applications to the fault diagnosis of rotating machines, 2022, published in Mechanism and Machine Theory
  • An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis, 2020, published in Structural Health Monitoring

Zhu frequently collaborates with several co-authors, indicating ongoing partnerships within the research community. These frequent collaborators include:

  • Weiguo Huang
  • Changqing Shen
  • Juanjuan Shi
  • Xingxing Jiang
  • Chuancang Ding

Best Publications

  • Multi-scale deep intra-class transfer learning for bearing fault diagnosis

    Xu Wang;Changqing Shen;Min Xia;Dong Wang

  • Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery

    Yumei Qi;Changqing Shen;Dong Wang;Juanjuan Shi

  • Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis

    Shibin Wang;Weiguo Huang;Z.K. Zhu

  • Bearing fault diagnosis via generalized logarithm sparse regularization

    Ziwei Zhang;Weiguo Huang;Yi Liao;Zeshu Song

  • A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines

    Xingxing Jiang;Jun Wang;Juanjuan Shi;Changqing Shen

  • Fault diagnosis of rotating machines based on the EMD manifold

    Jun Wang;Guifu Du;Zhongkui Zhu;Changqing Shen

  • Initial center frequency-guided VMD for fault diagnosis of rotating machines

    Xingxing Jiang;Changqing Shen;Juanjuan Shi;Zhongkui Zhu

  • Central frequency mode decomposition and its applications to the fault diagnosis of rotating machines

    Unknown

  • An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder

    Changqing Shen;Yumei Qi;Jun Wang;Gaigai Cai

  • Knowledge mapping-based adversarial domain adaptation: A novel fault diagnosis method with high generalizability under variable working conditions

    Qi Li;Changqing Shen;Liang Chen;Zhongkui Zhu

  • Time-Frequency Squeezing and Generalized Demodulation Combined for Variable Speed Bearing Fault Diagnosis

    Weiguo Huang;Guanqi Gao;Ning Li;Xingxing Jiang

  • Adaptive spectral kurtosis filtering based on Morlet wavelet and its application for signal transients detection

    Haiyang Liu;Weiguo Huang;Weiguo Huang;Shibin Wang;Zhongkui Zhu;Zhongkui Zhu

  • Moment matching-based intraclass multisource domain adaptation network for bearing fault diagnosis

    Unknown

  • An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis

    Xingxing Jiang;Jun Wang;Changqing Shen;Juanjuan Shi

  • Multiple Enhanced Sparse Decomposition for Gearbox Compound Fault Diagnosis

    Ning Li;Weiguo Huang;Wenjun Guo;Guanqi Gao

  • Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction

    Wei Fan;Gaigai Cai;Gaigai Cai;Z.K. Zhu;Z.K. Zhu;Changqing Shen

  • Cyclostationarity analysis for gearbox condition monitoring: Approaches and effectiveness

    Z.K. Zhu;Z.H. Feng;F.R. Kong

  • Detection of signal transients based on wavelet and statistics for machine fault diagnosis

    Z.K. Zhu;Ruqiang Yan;Liheng Luo;Z.H. Feng

  • Nonlocal theoretical approaches and atomistic simulations for longitudinal free vibration of nanorods/nanotubes and verification of different nonlocal models

    Cheng Li;Shuang Li;Linquan Yao;Zhongkui Zhu

  • Adaptive deep feature learning network with Nesterov momentum and its application to rotating machinery fault diagnosis

    Shenghao Tang;Changqing Shen;Dong Wang;Shuang Li

  • Smart multichannel mode extraction for enhanced bearing fault diagnosis

    Unknown

  • Self-Adaptive Multivariate Variational Mode Decomposition and Its Application for Bearing Fault Diagnosis

    Unknown

  • A Wavelet-Based Statistical Approach for Monitoring and Diagnosis of Compound Faults With Application to Rolling Bearings

    Wei Fan;Qiang Zhou;Jian Li;Zhongkui Zhu

  • Transient signal analysis based on Levenberg–Marquardt method for fault feature extraction of rotating machines

    Shibin Wang;Shibin Wang;Gaigai Cai;Zhongkui Zhu;Zhongkui Zhu;Weiguo Huang

  • Sparsity-enhanced signal decomposition via generalized minimax-concave penalty for gearbox fault diagnosis

    Gaigai Cai;Gaigai Cai;Ivan W. Selesnick;Shibin Wang;Shibin Wang;Weiwei Dai

  • Nonconvex Group Sparsity Signal Decomposition via Convex Optimization for Bearing Fault Diagnosis

    Weiguo Huang;Ning Li;Ivan Selesnick;Juanjuan Shi

Frequent Co-Authors

Changqing Shen
Changqing Shen Soochow University
Xingxing Jiang
Xingxing Jiang Soochow University
Dong Wang
Dong Wang Shanghai Jiao Tong University
Wei You
Wei You University of North Carolina at Chapel Hill
Cristian Garcia
Cristian Garcia Andrés Bello University
Jose Rodriguez
Jose Rodriguez San Sebastián University
Ivan W. Selesnick
Ivan W. Selesnick New York University
Fanrang Kong
Fanrang Kong University of Science and Technology of China
Yihua Hu
Yihua Hu University of York
Qingbo He
Qingbo He Shanghai Jiao Tong University

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