Meng Ma

2.2k total citations · 2 hit papers
39 papers, 1.8k citations indexed

About

Meng Ma is a scholar working on Control and Systems Engineering, Mechanics of Materials and Mechanical Engineering. According to data from OpenAlex, Meng Ma has authored 39 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Control and Systems Engineering, 13 papers in Mechanics of Materials and 13 papers in Mechanical Engineering. Recurrent topics in Meng Ma's work include Machine Fault Diagnosis Techniques (15 papers), Fault Detection and Control Systems (12 papers) and Gear and Bearing Dynamics Analysis (6 papers). Meng Ma is often cited by papers focused on Machine Fault Diagnosis Techniques (15 papers), Fault Detection and Control Systems (12 papers) and Gear and Bearing Dynamics Analysis (6 papers). Meng Ma collaborates with scholars based in China, United States and United Kingdom. Meng Ma's co-authors include Chuang Sun, Zhu Mao, Xuefeng Chen, Zhibin Zhao, Ruqiang Yan, Shaohua Tian, Zhi Zhai, Shibin Wang, Xingwu Zhang and Baoqing Ding and has published in prestigious journals such as Journal of Applied Physics, IEEE Access and Sensors.

In The Last Decade

Meng Ma

33 papers receiving 1.7k citations

Hit Papers

Deep Transfer Learning Based on Sparse Autoencoder for Re... 2018 2026 2020 2023 2018 2020 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. Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing
    2018 422 citations Chuang Sun, Meng Ma et al. IEEE Transactions on Industrial Informatics profile →
  2. Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction
    2020 356 citations Meng Ma, Zhu Mao IEEE Transactions on Industrial Informatics profile →

Peers

Meng Ma
Yiwei Cheng China
Xiaohang Jin China
Brigitte Chebel‐Morello France
Weining Lu China
Tongyang Pan China
Yuyan Zhang China
Xianguang Kong China
Van Tung Tran South Korea
Zheng Zhou China
Yiwei Cheng China
Meng Ma
Citations per year, relative to Meng Ma Meng Ma (= 1×) peers Yiwei Cheng

Countries citing papers authored by Meng Ma

Since Specialization
Citations

This map shows the geographic impact of Meng Ma'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 Meng Ma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Meng Ma more than expected).

Fields of papers citing papers by Meng Ma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Meng Ma. 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 Meng Ma. The network helps show where Meng Ma may publish in the future.

Co-authorship network of co-authors of Meng Ma

This figure shows the co-authorship network connecting the top 25 collaborators of Meng Ma. A scholar is included among the top collaborators of Meng Ma 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 Meng Ma. Meng Ma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhu, Man, Meng Ma, Jiashun Zheng, et al.. (2025). Reduction of DNA Topoisomerase Top2 Reprograms the Epigenetic Landscape and Extends Health and Life Span Across Species. Aging Cell. 24(6). e70010–e70010.
2.
Zhou, Bin, Haitao Wang, Jin Xie, et al.. (2025). Suppression of circ_0091761 ameliorates acute myocardial infarction-induced endothelial injury through regulation of miR-1278. Hereditas. 162(1). 169–169. 1 indexed citations
3.
Ma, Meng & Junjie Zhu. (2024). Interpretable Recurrent Variational State-Space Model for Fault Detection of Complex Systems Based on Multisensory Signals. Applied Sciences. 14(9). 3772–3772. 1 indexed citations
4.
Fu, Liu, Meng Ma, & Zhi Zhai. (2024). Deep Koopman Predictors for Anomaly Detection of Complex IoT Systems With Time Series Data. IEEE Internet of Things Journal. 11(23). 38360–38369. 5 indexed citations
5.
Ma, Meng, et al.. (2024). A Novel Optimal Sensor Placement Method for Optimizing the Diagnosability of Liquid Rocket Engine. Aerospace. 11(3). 239–239. 4 indexed citations
6.
7.
Wang, Zhizhen, et al.. (2024). Extended ARMA graph neural networks for the prognosis of complex systems. Knowledge-Based Systems. 309. 112762–112762.
8.
Ma, Meng, Liu Fu, Zhi Zhai, & Ruo-Bin Sun. (2024). Transformer based Kalman Filter with EM algorithm for time series prediction and anomaly detection of complex systems. Measurement. 229. 114378–114378. 15 indexed citations
9.
Ma, Meng, et al.. (2024). Adaptive physics-encoded graph neural network for health stage assessment of liquid-propellant rocket engines. Measurement Science and Technology. 35(8). 86144–86144. 2 indexed citations
10.
Zhang, Xiaoguang, et al.. (2024). Active Packet Dropout Kalman Filter Based Diagnosis Scheme For Liquid Rocket Engines With Measurement Fault. Journal of Physics Conference Series. 2762(1). 12016–12016. 1 indexed citations
11.
12.
Ma, Meng, et al.. (2024). Physics-Embedded Recurrent Graph Neural Network for Fault Diagnosis of Complex Systems. IEEE Access. 12. 122426–122436. 1 indexed citations
13.
Ma, Meng & Zhu Mao. (2020). Deep wavelet sequence-based gated recurrent units for the prognosis of rotating machinery. Structural Health Monitoring. 20(4). 1794–1804. 25 indexed citations
14.
Ma, Meng, Chuang Sun, Zhu Mao, & Xuefeng Chen. (2020). Ensemble deep learning with multi-objective optimization for prognosis of rotating machinery. ISA Transactions. 113. 166–174. 23 indexed citations
15.
Ma, Meng & Zhu Mao. (2020). Deep-Convolution-Based LSTM Network for Remaining Useful Life Prediction. IEEE Transactions on Industrial Informatics. 17(3). 1658–1667. 356 indexed citations breakdown →
16.
Ma, Meng, et al.. (2019). Direct waveform extraction via a deep recurrent denoising autoencoder. 61–61. 4 indexed citations
17.
Ma, Meng, et al.. (2019). Subspace-based MVE for performance degradation assessment of aero-engine bearings with multimodal features. Mechanical Systems and Signal Processing. 124. 298–312. 24 indexed citations
18.
19.
Ma, Meng, et al.. (2018). Deep Coupling Autoencoder for Fault Diagnosis With Multimodal Sensory Data. IEEE Transactions on Industrial Informatics. 14(3). 1137–1145. 235 indexed citations
20.
Si, Yafei, et al.. (2017). Catastrophic healthcare expenditure and its inequality for households with hypertension: evidence from the rural areas of Shaanxi Province in China. International Journal for Equity in Health. 16(1). 27–27. 54 indexed citations

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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