Feng Jia

10.0k total citations · 10 hit papers
48 papers, 8.2k citations indexed

About

Feng Jia is a scholar working on Control and Systems Engineering, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Feng Jia has authored 48 papers receiving a total of 8.2k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Control and Systems Engineering, 20 papers in Mechanical Engineering and 12 papers in Mechanics of Materials. Recurrent topics in Feng Jia's work include Machine Fault Diagnosis Techniques (28 papers), Fault Detection and Control Systems (15 papers) and Gear and Bearing Dynamics Analysis (11 papers). Feng Jia is often cited by papers focused on Machine Fault Diagnosis Techniques (28 papers), Fault Detection and Control Systems (15 papers) and Gear and Bearing Dynamics Analysis (11 papers). Feng Jia collaborates with scholars based in China, Germany and Canada. Feng Jia's co-authors include Yaguo Lei, Jing Lin, Saibo Xing, Naipeng Li, Bin Yang, Na Lü, Xinwei Jiang, Asoke K. Nandi, Liang Guo and Xin Zhou and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, IEEE Access and Sensors.

In The Last Decade

Feng Jia

44 papers receiving 7.9k citations

Hit Papers

5 papers align trajectories

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.

Applications of machine learning to machine fault diagnosis: A review and roadmap

2020 2.0k citations Yaguo Lei, Bin Yang et al. Mechanical Systems and Signal Processing profile →
Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

2015 1.4k citations Feng Jia, Yaguo Lei et al. Mechanical Systems and Signal Processing profile →
An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

2016 984 citations Yaguo Lei, Feng Jia et al. IEEE Transactions on Industrial Electronics profile →
A recurrent neural network based health indicator for remaining useful life prediction of bearings

2017 959 citations Liang Guo, Feng Jia et al. Neurocomputing profile →
An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings

2019 719 citations Bin Yang, Yaguo Lei et al. Mechanical Systems and Signal Processing profile →
Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization

2018 498 citations Feng Jia, Yaguo Lei et al. Mechanical Systems and Signal Processing profile →
A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines

2017 443 citations Feng Jia, Yaguo Lei et al. Neurocomputing profile →
A Polynomial Kernel Induced Distance Metric to Improve Deep Transfer Learning for Fault Diagnosis of Machines

2019 226 citations Bin Yang, Yaguo Lei et al. IEEE Transactions on Industrial Electronics profile →
Multi-scale integrated deep self-attention network for predicting remaining useful life of aero-engine

2023 148 citations Ke Zhao, Zhen Jia et al. Engineering Applications of Artificial Intelligence profile →
A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains

2022 114 citations Ke Zhao, Feng Jia et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Feng Jia China	18	6.5k	3.9k	2.3k	1.2k	621	48	8.2k
Chuang Sun China	44	5.2k 0.8×	2.8k 0.7×	1.5k 0.6×	1.5k 1.2×	784 1.3×	127	7.2k
Haidong Shao China	50	8.0k 1.2×	4.6k 1.2×	2.7k 1.2×	1.8k 1.5×	759 1.2×	128	9.9k
Hongkai Jiang China	47	6.8k 1.0×	4.1k 1.1×	2.3k 1.0×	1.3k 1.0×	521 0.8×	132	8.0k
Jinglong Chen China	45	5.4k 0.8×	3.0k 0.8×	1.6k 0.7×	1.4k 1.1×	412 0.7×	188	7.1k
Baoping Tang China	44	5.6k 0.9×	3.4k 0.9×	1.8k 0.8×	851 0.7×	410 0.7×	209	7.7k
Liang Guo China	32	5.1k 0.8×	3.3k 0.9×	1.6k 0.7×	874 0.7×	681 1.1×	141	7.2k
Minping Jia China	48	4.9k 0.8×	3.2k 0.8×	1.6k 0.7×	1.1k 0.9×	592 1.0×	154	6.8k
Changqing Shen China	41	5.6k 0.9×	3.4k 0.9×	1.8k 0.8×	1.1k 0.9×	361 0.6×	199	6.8k
Zhibin Zhao China	40	4.0k 0.6×	2.2k 0.6×	1.1k 0.5×	1.3k 1.1×	514 0.8×	148	6.3k
Yanyang Zi China	46	7.1k 1.1×	5.0k 1.3×	2.5k 1.1×	580 0.5×	292 0.5×	177	9.2k

Countries citing papers authored by Feng Jia

Since Specialization

Citations

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

Fields of papers citing papers by Feng Jia

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Feng Jia

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Jia, Feng, Xiang Xu, Yuanfei Wang, & Jianjun Shen. (2025). A novel source-free domain adaptation network for intelligent diagnosis of bearings under unknown faults. Measurement Science and Technology. 36(4). 46129–46129. 2 indexed citations

Jia, Feng, et al.. (2024). Stepwise feature norm network with adaptive weighting for open set cross-domain intelligent fault diagnosis of bearings. Measurement Science and Technology. 35(5). 56126–56126. 5 indexed citations

Jia, Feng, et al.. (2024). Accurate recognition of micromorphology images of epoxy coatings for deep-sea environments based on a deep learning super-resolution method. Corrosion Communications. 19. 14–27. 3 indexed citations

Shen, Jianjun, et al.. (2024). Co-simulation for optimal working parameter selection during soil vibratory compaction process. Journal of Terramechanics. 112. 45–57. 2 indexed citations

Zhao, Ke, Zhen Jia, Feng Jia, & Haidong Shao. (2023). Multi-scale integrated deep self-attention network for predicting remaining useful life of aero-engine. Engineering Applications of Artificial Intelligence. 120. 105860–105860. 148 indexed citations breakdown →

Wang, Yuanfei, et al.. (2022). Multi-Domain Weighted Transfer Adversarial Network for the Cross-Domain Intelligent Fault Diagnosis of Bearings. Machines. 10(5). 326–326. 5 indexed citations

Zhao, Ke, Feng Jia, & Haidong Shao. (2022). Unbalanced fault diagnosis of rolling bearings using transfer adaptive boosting with squeeze-and-excitation attention convolutional neural network. Measurement Science and Technology. 34(4). 44006–44006. 16 indexed citations

Jia, Feng, et al.. (2021). Deep transfer attention network for intelligent fault diagnosis of rolling bearings. Journal of Physics Conference Series. 1983(1). 12011–12011.

Li, Yiqing, Yan Cao, & Feng Jia. (2021). A Neural Network Based Dynamic Control Method for Soft Pneumatic Actuator with Symmetrical Chambers. Actuators. 10(6). 112–112. 8 indexed citations

10.

Jia, Feng, et al.. (2021). The Open Set Weighted Domain Adaptation Method for Fault Diagnosis of Rolling Bearings. 1–6.

11.

Meng, Yongqing, et al.. (2021). Variable Voltage Variable Frequency Modular Multilevel AC/AC Converter With High-Frequency Harmonics Filtering Capability. IEEE Journal of Emerging and Selected Topics in Power Electronics. 10(1). 811–821. 8 indexed citations

12.

Zuo, Hao, Yixin Chen, & Feng Jia. (2020). A new C0 layerwise wavelet finite element formulation for the static and free vibration analysis of composite plates. Composite Structures. 254. 112852–112852. 13 indexed citations

13.

Xing, Saibo, Yaguo Lei, Shuhui Wang, & Feng Jia. (2020). Distribution-Invariant Deep Belief Network for Intelligent Fault Diagnosis of Machines Under New Working Conditions. IEEE Transactions on Industrial Electronics. 68(3). 2617–2625. 136 indexed citations

14.

Shen, Jianjun, et al.. (2020). Research on coupling dynamics and coordinated control of a legged robot. Journal of Vibration and Control. 27(19-20). 2385–2399. 3 indexed citations

15.

Yang, Bin, Yaguo Lei, Feng Jia, & Saibo Xing. (2019). An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mechanical Systems and Signal Processing. 122. 692–706. 719 indexed citations breakdown →

16.

Jia, Feng, Yaguo Lei, Liang Guo, Jing Lin, & Saibo Xing. (2017). A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines. Neurocomputing. 272. 619–628. 443 indexed citations breakdown →

17.

Lei, Yaguo, Xin Zhou, Xuefang Xu, & Feng Jia. (2017). A dirty data recognition method for machinery condition monitoring in big data era. IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. 7061–7066. 5 indexed citations

18.

Jia, Feng, Yaguo Lei, Saibo Xing, & Jing Lin. (2016). A method of automatic feature extraction from massive vibration signals of machines. 9. 1–6. 7 indexed citations

19.

Jia, Feng, et al.. (2008). A Change Management Framework: Managing the Error Variations in Multi-stage Machining Processes. 32. 3660–3663. 2 indexed citations

20.

Jia, Feng. (2000). A KDD MODEL BASED ON CONVERSION OF QUANTITY QUALITY FEATURES OF ATTRIBUTES. 3 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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