Changqing Shen

Changqing Shen publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Changqing Shen sits on this spectrum.

Changqing Shen D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Changqing Shen sits on this spectrum.

Research.com Recognitions

• 2025 - Research.com Rising Stars Award

Overview

Best Publications

• Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis

  Xiaojie Guo;Liang Chen;Changqing Shen

  801
  Citations

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

  Xu Wang;Changqing Shen;Min Xia;Dong Wang

  289
  Citations

• A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions

  Jun Zhu;Nan Chen;Changqing Shen

  284
  Citations

• Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier

  Changqing Shen;Changqing Shen;Dong Wang;Fanrang Kong;Peter W. Tse

  281
  Citations

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

  Yumei Qi;Changqing Shen;Dong Wang;Juanjuan Shi

  251
  Citations

• A New Deep Transfer Learning Method for Bearing Fault Diagnosis Under Different Working Conditions

  Jun Zhu;Nan Chen;Changqing Shen

  234
  Citations

• Bearing fault diagnosis via generalized logarithm sparse regularization

  Ziwei Zhang;Weiguo Huang;Yi Liao;Zeshu Song

  219
  Citations

• 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

  217
  Citations

• Fault diagnosis of rotating machines based on the EMD manifold

  Jun Wang;Guifu Du;Zhongkui Zhu;Changqing Shen

  215
  Citations

• Adversarial domain-invariant generalization: a generic domain-regressive framework for bearing fault diagnosis under unseen conditions

  Liang Chen;Qi Li;Changqing Shen;Jun Zhu

  199
  Citations

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

  Xingxing Jiang;Changqing Shen;Juanjuan Shi;Zhongkui Zhu

  193
  Citations

• A New Multiple Source Domain Adaptation Fault Diagnosis Method Between Different Rotating Machines

  Jun Zhu;Nan Chen;Changqing Shen

  175
  Citations

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

  Changqing Shen;Yumei Qi;Jun Wang;Gaigai Cai

  164
  Citations

• 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

  145
  Citations

• Fully interpretable neural network for locating resonance frequency bands for machine condition monitoring

  Dong Wang;Yikai Chen;Changqing Shen;Jingjing Zhong

  134
  Citations

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

  Xingxing Jiang;Jun Wang;Changqing Shen;Juanjuan Shi

  127
  Citations

• 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

  123
  Citations

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

  Shenghao Tang;Changqing Shen;Dong Wang;Shuang Li

  94
  Citations

• Multi-sensor gearbox fault diagnosis by using feature-fusion covariance matrix and multi-Riemannian kernel ridge regression

  Xin Li;Xiang Zhong;Haidong Shao;Te Han

  86
  Citations

• Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

  Xiaojie Guo;Changqing Shen;Liang Chen

  83
  Citations

• A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis

  Changqing Shen;Changqing Shen;Qingbo He;Fanrang Kong;Peter W Tse

  64
  Citations

• An End-to-End Model Based on Improved Adaptive Deep Belief Network and Its Application to Bearing Fault Diagnosis

  Jiaqi Xie;Guifu Du;Changqing Shen;Nan Chen

  61
  Citations

• Dynamic Joint Distribution Alignment Network for Bearing Fault Diagnosis Under Variable Working Conditions

  Changqing Shen;Xu Wang;Dong Wang;Yongxiang Li

  59
  Citations