Changqing Shen

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