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Haidong Shao

Haidong Shao

Award Badge
Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
50
Citations
10329
World Ranking
320
National Ranking
107

Engineering and Technology

D-Index
53
Citations
12535
World Ranking
3350
National Ranking
684

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Haidong Shao is a researcher affiliated with Hunan University in China, specializing in engineering with a focus on control and systems engineering, mechanical engineering, artificial intelligence, mechanics of materials, and industrial and manufacturing engineering. Their work predominantly engages with machine fault diagnosis and related reliability and control topics.

Their research covers key topics such as:

  • Machine Fault Diagnosis Techniques
  • Fault Detection and Control Systems
  • Gear and Bearing Dynamics Analysis
  • Anomaly Detection Techniques and Applications
  • Engineering Diagnostics and Reliability
  • Industrial Vision Systems and Defect Detection
  • Machine Learning in Bioinformatics

Haidong Shao has contributed to multiple scholarly articles, including numerous papers published in influential venues. Frequent publication platforms include:

  • Reliability Engineering & System Safety
  • Measurement Science and Technology
  • Advanced Engineering Informatics
  • Knowledge-Based Systems
  • IEEE Transactions on Industrial Informatics

Among recent significant papers authored or co-authored by Haidong Shao are:

  • "Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images" (2020), published in IEEE Transactions on Industrial Informatics
  • "A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance" (2021), published in Information Fusion

In their collaborations, Shao frequently works with peers who have contributed extensively to the field, including Bin Liu, Yiming Xiao, Junsheng Cheng, Shen Yan, and Yu Yang.

Their scholarly activity largely centers on the intersection of engineering disciplines and artificial intelligence to advance diagnostic methodologies for machinery and fault detection. This includes research on thermal imaging, deep transfer learning, and multisensory fusion techniques applied to maintenance and reliability systems.

Best Publications

  • A novel deep autoencoder feature learning method for rotating machinery fault diagnosis

    Haidong Shao;Hongkai Jiang;Huiwei Zhao;Fuan Wang

  • Rolling bearing fault diagnosis using an optimization deep belief network

    Haidong Shao;Hongkai Jiang;Xun Zhang;Maogui Niu

  • Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network

    Haidong Shao;Hongkai Jiang;Haizhou Zhang;Tianchen Liang

  • A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

    Haidong Shao;Hongkai Jiang;Ying Lin;Xingqiu Li

  • Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images

    Haidong Shao;Min Xia;Guangjie Han;Yu Zhang

  • Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine

    Unknown

  • Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing

    Haidong Shao;Hongkai Jiang;Haizhou Zhang;Wenjing Duan

  • A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation

    Min Xia;Haidong Shao;Xiandong Ma;Clarence W. de Silva

  • Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning

    Min Xia;Haidong Shao;Darren Williams;Siliang Lu

  • An enhancement deep feature fusion method for rotating machinery fault diagnosis

    Haidong Shao;Hongkai Jiang;Fuan Wang;Huiwei Zhao

  • LiConvFormer: A lightweight fault diagnosis framework using separable multiscale convolution and broadcast self-attention

    Unknown

  • Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet.

    Haidong Shao;Hongkai Jiang;Fuan Wang;Yanan Wang

  • A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance

    Haidong Shao;Haidong Shao;Jing Lin;Liangwei Zhang;Diego Galar

  • Unsupervised domain-share CNN for machine fault transfer diagnosis from steady speeds to time-varying speeds

    Hongru Cao;Haidong Shao;Haidong Shao;Xiang Zhong;Qianwang Deng

  • Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing

    Unknown

  • Collaborative fault diagnosis of rotating machinery via dual adversarial guided unsupervised multi-domain adaptation network

    Unknown

  • Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder

    He Zhiyi;Shao Haidong;Shao Haidong;Jing Lin;Cheng Junsheng

  • An adaptive deep convolutional neural network for rolling bearing fault diagnosis

    Unknown

  • Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions

    Zhiyi He;Haidong Shao;Xiang Zhong;Xianzhu Zhao

  • Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples

    Zhiyi He;Haidong Shao;Haidong Shao;Ping Wang;Janet (Jing) Lin

  • Modified Stacked Auto-encoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery

    Haidong Shao;Min Xia;Jiafu Wan;Clarence De Silva

  • Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network

    Hongkai Jiang;Xingqiu Li;Haidong Shao;Ke Zhao

  • Rolling bearing fault detection using continuous deep belief network with locally linear embedding

    Haidong Shao;Hongkai Jiang;Xingqiu Li;Tianchen Liang

  • Modified Deep Autoencoder Driven by Multisource Parameters for Fault Transfer Prognosis of Aeroengine

    Zhiyi He;Haidong Shao;Ziyang Ding;Hongkai Jiang

  • Improved Deep Transfer Auto-Encoder for Fault Diagnosis of Gearbox Under Variable Working Conditions With Small Training Samples

    Zhiyi He;Haidong Shao;Xiaoyang Zhang;Junsheng Cheng

  • Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network

    Xingqiu Li;Hongkai Jiang;Xiong Xiong;Haidong Shao

  • A Fusion CWSMM-based Framework for Rotating Machinery Fault Diagnosis under Strong Interference and Imbalanced Case

    Xin Li;Jian Cheng;Haidong Shao;Kan Liu

Frequent Co-Authors

Hongkai Jiang
Hongkai Jiang Northwestern Polytechnical University
Junsheng Cheng
Junsheng Cheng Hunan University
Clarence W. de Silva
Clarence W. de Silva University of British Columbia
Jing Lin
Jing Lin Shenzhen University
Jiafu Wan
Jiafu Wan South China University of Technology
Changqing Shen
Changqing Shen Soochow University
Siliang Lu
Siliang Lu Anhui University
Baoping Cai
Baoping Cai China University of Petroleum, Beijing

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