Maogui Niu

961 total citations · 1 hit paper
8 papers, 803 citations indexed

About

Maogui Niu is a scholar working on Control and Systems Engineering, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Maogui Niu has authored 8 papers receiving a total of 803 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Control and Systems Engineering, 6 papers in Mechanical Engineering and 1 paper in Mechanics of Materials. Recurrent topics in Maogui Niu's work include Machine Fault Diagnosis Techniques (7 papers), Gear and Bearing Dynamics Analysis (4 papers) and Advanced Sensor and Control Systems (2 papers). Maogui Niu is often cited by papers focused on Machine Fault Diagnosis Techniques (7 papers), Gear and Bearing Dynamics Analysis (4 papers) and Advanced Sensor and Control Systems (2 papers). Maogui Niu collaborates with scholars based in China. Maogui Niu's co-authors include Hongkai Jiang, Haidong Shao, Xun Zhang, Xingqiu Li, Ruixin Wang, Xiong Xiong, Zhenghong Wu, Chao Zhang, Long Chen and Yong Zhou and has published in prestigious journals such as Mechanical Systems and Signal Processing, Knowledge-Based Systems and Measurement Science and Technology.

In The Last Decade

Maogui Niu

7 papers receiving 762 citations

Hit Papers

Rolling bearing fault diagnosis using an optimization dee... 2015 2026 2018 2022 2015 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Rolling bearing fault diagnosis using an optimization deep belief network
    2015 430 citations Haidong Shao, Hongkai Jiang et al. Measurement Science and Technology profile →

Peers

Maogui Niu
Kun Xu China
Qinghua Zhang China
Weiwei Qian China
Aisong Qin China
Alexander E. Prosvirin South Korea
Joon Ha Jung South Korea
Wei-Li Qin China
Yiyao An China
Mohd Syahril Ramadhan Mohd Saufi Malaysia
Jianyu Wang China
Kun Xu China
Maogui Niu
Citations per year, relative to Maogui Niu Maogui Niu (= 1×) peers Kun Xu

Countries citing papers authored by Maogui Niu

Since Specialization
Citations

This map shows the geographic impact of Maogui Niu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Maogui Niu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maogui Niu more than expected).

Fields of papers citing papers by Maogui Niu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maogui Niu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Maogui Niu. The network helps show where Maogui Niu may publish in the future.

Co-authorship network of co-authors of Maogui Niu

This figure shows the co-authorship network connecting the top 25 collaborators of Maogui Niu. A scholar is included among the top collaborators of Maogui Niu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Maogui Niu. Maogui Niu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

8 of 8 papers shown
1.
Niu, Maogui, Hongkai Jiang, & Haidong Shao. (2025). Dynamic weighted adversarial domain adaptation network with sparse representation denoising module for rotating machinery fault diagnosis. Engineering Applications of Artificial Intelligence. 142. 109963–109963. 8 indexed citations
2.
Niu, Maogui, Hongkai Jiang, Zhenghong Wu, & Haidong Shao. (2024). An enhanced sparse autoencoder for machinery interpretable fault diagnosis. Measurement Science and Technology. 35(5). 55108–55108. 6 indexed citations
3.
Niu, Maogui, Hongkai Jiang, & Renhe Yao. (2024). Adaptive multiscale wavelet-guided periodic sparse representation for bearing incipient fault feature extraction. Science China Technological Sciences. 67(11). 3585–3596.
4.
Li, Xingqiu, Hongkai Jiang, Maogui Niu, & Ruixin Wang. (2020). An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm. Mechanical Systems and Signal Processing. 142. 106752–106752. 173 indexed citations
5.
Li, Xingqiu, Hongkai Jiang, Ruixin Wang, & Maogui Niu. (2020). Rolling bearing fault diagnosis using optimal ensemble deep transfer network. Knowledge-Based Systems. 213. 106695–106695. 126 indexed citations
6.
Xiong, Xiong, Hongkai Jiang, Xingqiu Li, & Maogui Niu. (2019). A Wasserstein gradient-penalty generative adversarial network with deep auto-encoder for bearing intelligent fault diagnosis. Measurement Science and Technology. 31(4). 45006–45006. 54 indexed citations
7.
8.
Shao, Haidong, Hongkai Jiang, Xun Zhang, & Maogui Niu. (2015). Rolling bearing fault diagnosis using an optimization deep belief network. Measurement Science and Technology. 26(11). 115002–115002. 430 indexed citations breakdown →

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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