Ling Xiang

3.9k total citations · 5 hit papers
110 papers, 2.9k citations indexed

About

Ling Xiang is a scholar working on Control and Systems Engineering, Mechanical Engineering and Electrical and Electronic Engineering. According to data from OpenAlex, Ling Xiang has authored 110 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 81 papers in Control and Systems Engineering, 63 papers in Mechanical Engineering and 22 papers in Electrical and Electronic Engineering. Recurrent topics in Ling Xiang's work include Machine Fault Diagnosis Techniques (59 papers), Gear and Bearing Dynamics Analysis (49 papers) and Tribology and Lubrication Engineering (22 papers). Ling Xiang is often cited by papers focused on Machine Fault Diagnosis Techniques (59 papers), Gear and Bearing Dynamics Analysis (49 papers) and Tribology and Lubrication Engineering (22 papers). Ling Xiang collaborates with scholars based in China, United Kingdom and Thailand. Ling Xiang's co-authors include Aijun Hu, Hao Su, Xin Yang, Aijun Hu, Xiaoan Yan, Yonggang Xu, Penghe Wang, Nan Gao, Yue Zhang and Minping Jia and has published in prestigious journals such as Chemical Engineering Journal, Applied Energy and IEEE Transactions on Power Electronics.

In The Last Decade

Ling Xiang

97 papers receiving 2.8k citations

Hit Papers

Fault detection of wind turbine based on SCADA data analy... 2021 2026 2022 2024 2021 2022 2021 2024 2024 50 100 150 200 250
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. Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism
    2021 277 citations Ling Xiang, Aijun Hu et al. profile →
  2. A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions
    2022 190 citations Hao Su, Ling Xiang et al. Mechanical Systems and Signal Processing profile →
  3. Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks
    2021 167 citations Ling Xiang, Aijun Hu et al. profile →
  4. MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios
    2024 65 citations Xiaoan Yan, Xing Hua et al. Expert Systems with Applications profile →
  5. CDTFAFN: A novel coarse-to-fine dual-scale time-frequency attention fusion network for machinery vibro-acoustic fault diagnosis
    2024 44 citations Xiaoan Yan, Dong Jiang et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ling Xiang China 32 1.9k 1.4k 601 573 368 110 2.9k
Aijun Hu China 23 1.3k 0.7× 783 0.5× 481 0.8× 361 0.6× 298 0.8× 80 2.0k
Minghang Zhao China 24 1.8k 1.0× 1.1k 0.8× 320 0.5× 569 1.0× 509 1.4× 59 3.0k
Shuilong He China 28 2.3k 1.2× 1.3k 0.9× 306 0.5× 679 1.2× 641 1.7× 84 3.0k
Anil Kumar China 32 2.1k 1.1× 1.6k 1.1× 335 0.6× 697 1.2× 378 1.0× 118 3.1k
Jian Ma China 23 1.6k 0.9× 924 0.6× 811 1.3× 589 1.0× 385 1.0× 113 2.9k
Gaoliang Peng China 20 2.3k 1.2× 1.7k 1.2× 228 0.4× 920 1.6× 352 1.0× 72 3.3k
Yuanhang Chen China 12 2.5k 1.3× 1.9k 1.3× 219 0.4× 990 1.7× 349 0.9× 15 3.2k
Levent Eren Türkiye 14 1.8k 1.0× 1.1k 0.8× 395 0.7× 591 1.0× 285 0.8× 36 2.5k

Countries citing papers authored by Ling Xiang

Since Specialization
Citations

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

Fields of papers citing papers by Ling Xiang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ling Xiang

This figure shows the co-authorship network connecting the top 25 collaborators of Ling Xiang. A scholar is included among the top collaborators of Ling Xiang 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 Ling Xiang. Ling Xiang 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.
Xiang, Ling, et al.. (2025). Memory-augmented prototypical meta-learning method for bearing fault identification under few-sample conditions. Neurocomputing. 635. 129996–129996. 3 indexed citations
2.
Hu, Aijun, et al.. (2025). Vibration and acoustic signal consistent feature fusion network for intelligent bearing fault diagnosis. Engineering Research Express. 7(3). 35206–35206. 1 indexed citations
3.
Hu, Aijun, et al.. (2025). A multi-domain Collaborative bearing data generation model for improving the comprehensive quality of generated samples. Engineering Applications of Artificial Intelligence. 164. 113324–113324.
4.
Xiang, Ling, et al.. (2024). A frequency channel-attention based vision Transformer method for bearing fault identification across different working conditions. Expert Systems with Applications. 262. 125686–125686. 12 indexed citations
5.
Yan, Xiaoan, et al.. (2024). MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios. Expert Systems with Applications. 259. 125214–125214. 65 indexed citations breakdown →
6.
Zhu, Guopeng, et al.. (2024). A novel stochastic process diffusion model for wind turbines condition monitoring and fault identification with multi-parameter information fusion. Mechanical Systems and Signal Processing. 214. 111397–111397. 22 indexed citations
7.
Su, Hao, et al.. (2024). Semi-Supervised Temporal Meta-Learning Framework for Wind Turbine Bearing Fault Diagnosis Under Limited Annotation Data. IEEE Transactions on Instrumentation and Measurement. 73. 1–9. 11 indexed citations
8.
Su, Hao, Ling Xiang, & Aijun Hu. (2024). Application of deep learning to fault diagnosis of rotating machineries. Measurement Science and Technology. 35(4). 42003–42003. 34 indexed citations
9.
Yan, Xiaoan, Xing Hua, Dong Jiang, & Ling Xiang. (2024). A novel robust intelligent fault diagnosis method for rolling bearings based on SPAVMD and WOA-LSSVM under noisy conditions. Measurement Science and Technology. 35(5). 56121–56121. 8 indexed citations
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12.
Hu, Aijun, et al.. (2023). A novel vision transformer network for rolling bearing remaining useful life prediction. Measurement Science and Technology. 35(2). 25106–25106. 6 indexed citations
13.
Hu, Aijun, et al.. (2022). Nonlinear Time-varying Parameter Dynamic Model of Rolling Bearing and Failure Mechanism Research. Journal of Mechanical Engineering. 58(19). 139–139. 2 indexed citations
14.
Ma, Chaoyong, et al.. (2022). Optimization of Ramanujan Subspace Periodic and Its Application in Identifying Industrial Bearing Fault Features. IEEE Transactions on Instrumentation and Measurement. 72. 1–7.
15.
Hu, Aijun, et al.. (2022). Rotating machinery fault diagnosis based on impact feature extraction deep neural network. Measurement Science and Technology. 33(11). 114004–114004. 22 indexed citations
16.
Xiang, Ling, et al.. (2022). A new condition-monitoring method based on multi-variable correlation learning network for wind turbine fault detection. Measurement Science and Technology. 34(2). 24009–24009. 13 indexed citations
17.
Xiang, Ling, et al.. (2022). Impact of Wind Power Penetration on Wind–Thermal-Bundled Transmission System. IEEE Transactions on Power Electronics. 37(12). 15616–15625. 22 indexed citations
18.
Hu, Aijun, et al.. (2019). Frequency Loss and Recovery in Rolling Bearing Fault Detection. Chinese Journal of Mechanical Engineering. 32(1). 5 indexed citations
19.
Hu, Aijun, et al.. (2019). An engineering condition indicator for condition monitoring of wind turbine bearings. Wind Energy. 23(2). 207–219. 21 indexed citations
20.
Hu, Aijun, et al.. (2017). A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing. Shock and Vibration. 2017. 1–11. 7 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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