Bing Song

1.5k total citations
80 papers, 1.0k citations indexed

About

Bing Song is a scholar working on Control and Systems Engineering, Mechanical Engineering and Statistics, Probability and Uncertainty. According to data from OpenAlex, Bing Song has authored 80 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 65 papers in Control and Systems Engineering, 47 papers in Mechanical Engineering and 14 papers in Statistics, Probability and Uncertainty. Recurrent topics in Bing Song's work include Fault Detection and Control Systems (60 papers), Mineral Processing and Grinding (45 papers) and Machine Fault Diagnosis Techniques (15 papers). Bing Song is often cited by papers focused on Fault Detection and Control Systems (60 papers), Mineral Processing and Grinding (45 papers) and Machine Fault Diagnosis Techniques (15 papers). Bing Song collaborates with scholars based in China, Canada and United States. Bing Song's co-authors include Hongbo Shi, Shuai Tan, Yang Tao, Hongbo Shi, Xinggui Zhou, Bo Zhao, Stephen C. Benz, Shahrooz Rabizadeh, Huaicheng Yan and Christopher W. Szeto and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, Optics Express and IEEE Access.

In The Last Decade

Bing Song

67 papers receiving 1.0k citations

Peers

Bing Song

CSE

SPU

Wenkai Hu China

Deyun Xiao China

Zhe Yang China

Hongpeng Yin China

Nader Fnaiech France

Gang Niu China

Mohamed-Salah Ouali Canada

Jun Yi China

Mohammad Anwar Hosen Australia

Wenkai Hu China

Bing Song

586 ×0.7

CSE

142 ×0.3

374 ×1.7

49 ×0.3

181 ×1.2

SPU

Citations per year, relative to Bing Song Bing Song (= 1×) peers Wenkai Hu

Countries citing papers authored by Bing Song

Since Specialization

Citations

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

Fields of papers citing papers by Bing Song

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bing Song

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

All Works

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20 of 20 papers shown

Shi, Hongbo, et al.. (2025). Fault-Influenced Variable Selection and Distributional Offset Evaluation of CVDs Graph Convolutional Neural Network for Nonlinear KPI-Related Process Monitoring. IEEE Transactions on Instrumentation and Measurement. 74. 1–13.

Ding, Z., Hongbo Shi, Bing Song, & Yang Tao. (2025). A Novel Adaptive Mechanism-Data Fusion Graph Embedding Network for Fault Diagnosis. IEEE Transactions on Industrial Informatics. 21(8). 6061–6070.

Shi, Hongbo, et al.. (2025). Fault Diagnosis of Unseen Modes in Chemical Process via Fusing Invariance and Specificity. IEEE Transactions on Instrumentation and Measurement. 74. 1–11. 1 indexed citations

Guo, Lei, Hongbo Shi, Shuai Tan, Bing Song, & Yang Tao. (2024). A knowledge-driven spatial-temporal graph neural network for quality-related fault detection. Process Safety and Environmental Protection. 184. 1512–1524. 13 indexed citations

Song, Bing, Ziyu Wang, Shuo Liu, et al.. (2024). Real-time distributed observing on regeneration rates of an apodized type I fiber Bragg grating. Optics Express. 32(18). 30982–30982. 1 indexed citations

Shi, Hongbo, et al.. (2024). Hierarchical Weighted LSTM with One-class Classifier for Preventive Protection of Cultural Heritage in Museums. Journal on Computing and Cultural Heritage. 18(1). 1–20.

Tan, Shuai, Yifan Wang, Hongbo Shi, Bing Song, & Yang Tao. (2023). A topology model based on common and specific feature separation for multimode process monitoring. Journal of Process Control. 130. 103052–103052. 1 indexed citations

Tao, Yang, Hongbo Shi, Bing Song, & Shuai Tan. (2023). A Distributed Adaptive Monitoring Method for Performance Indicator in Large-Scale Dynamic Process. IEEE Transactions on Industrial Informatics. 19(10). 10425–10433. 10 indexed citations

Shi, Hongbo, et al.. (2023). Semi-Supervised Relevance Variable Selection and Hierarchical Feature Regularization Variational Autoencoder for Nonlinear Quality-Related Process Monitoring. IEEE Transactions on Instrumentation and Measurement. 72. 1–11. 5 indexed citations

10.

Shi, Hongbo, et al.. (2023). Temporal Attention Source-Free Adaptation for Chemical Processes Fault Diagnosis. IEEE Transactions on Industrial Informatics. 20(3). 4773–4783. 6 indexed citations

11.

Shi, Hongbo, et al.. (2023). Sensor Fault Detection and Diagnosis Using Graph Convolutional Network Combining Process Knowledge and Process Data. IEEE Transactions on Instrumentation and Measurement. 72. 1–10. 12 indexed citations

12.

Shi, Hongbo, et al.. (2022). Fault Diagnosis of Unseen Modes in Chemical Processes Based on Labeling and Class Progressive Adversarial Learning. IEEE Transactions on Instrumentation and Measurement. 72. 1–12. 10 indexed citations

13.

Shi, Hongbo, et al.. (2022). Weighted Conditional Discriminant Analysis for Unseen Operating Modes Fault Diagnosis in Chemical Processes. IEEE Transactions on Instrumentation and Measurement. 71. 1–14. 10 indexed citations

14.

Tan, Shuai, et al.. (2022). A Novel Multiview Predictive Local Adversarial Network for Partial Transfer Learning in Cross-Domain Fault Diagnostics. IEEE Transactions on Instrumentation and Measurement. 72. 1–12. 16 indexed citations

15.

Shi, Hongbo, et al.. (2021). Convolutional Neural Network Based Feature Learning for Large-Scale Quality-Related Process Monitoring. IEEE Transactions on Industrial Informatics. 18(7). 4555–4565. 41 indexed citations

16.

Tao, Yang, Hongbo Shi, Bing Song, & Shuai Tan. (2021). Hierarchical Latent Variable Extraction and Multisegment Probability Density Analysis Method for Incipient Fault Detection. IEEE Transactions on Industrial Informatics. 18(4). 2244–2254. 12 indexed citations

17.

Tan, Shuai, et al.. (2021). Improved Ensemble Feature Selection Based on DT for KPI Prediction. IEEE Access. 9. 136861–136871. 1 indexed citations

18.

Song, Bing, Hongbo Shi, Shuai Tan, & Yang Tao. (2020). Multisubspace Orthogonal Canonical Correlation Analysis for Quality-Related Plant-Wide Process Monitoring. IEEE Transactions on Industrial Informatics. 17(9). 6368–6378. 56 indexed citations

19.

Song, Bing, Huaicheng Yan, Hongbo Shi, & Shuai Tan. (2019). Multisubspace Elastic Network for Multimode Quality-Related Process Monitoring. IEEE Transactions on Industrial Informatics. 16(9). 5874–5883. 41 indexed citations

20.

Song, Bing, Xinggui Zhou, Hongbo Shi, & Yang Tao. (2018). Performance-Indicator-Oriented Concurrent Subspace Process Monitoring Method. IEEE Transactions on Industrial Electronics. 66(7). 5535–5545. 72 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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