The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Zhipeng Feng sits on this spectrum.
This scientist: 109 publications — 11th percentile
11% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 804 publications or more.
The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Zhipeng Feng sits on this spectrum.
This scientist: 46 D-Index — 49th percentile
49% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 107 D-Index or more.
Zhipeng Feng is affiliated with the University of Science and Technology Beijing in China and focuses on engineering research, particularly in mechanical engineering and related subfields. Their work spans multiple aspects of machine diagnostics, fault detection, and reliability analysis within mechanical systems.
The scientist has contributed extensively to the field of Mechanical Engineering with a substantial number of publications. Their research interests primarily include:
Zhipeng Feng's recent published papers detail investigations into fault diagnosis methods for planetary gearboxes and rotating machinery under varying operational conditions. Some notable works include:
The majority of Feng's contributions have been published in the journal Mechanical Systems and Signal Processing, where they have authored 16 papers. Other frequent publication venues include:
They maintain collaboration with several researchers in their field, with frequent coauthors including Xiaowang Chen, Tianyang Wang, Fulei Chu, Dong Zhang, and Rongzhou Lin, each contributing to improving fault diagnosis technologies and related engineering disciplines.
The research mainly falls under the broader field of Engineering, contributing to the advancement of diagnostics and control in mechanical systems through detailed analysis techniques and innovative signal processing methods. These efforts contribute to addressing challenges in industrial machinery maintenance and reliability monitoring.
Zhipeng Feng;Ming Liang;Fulei Chu
Zhipeng Feng;Ming J. Zuo
Tianyang Wang;Qinkai Han;Fulei Chu;Zhipeng Feng
Xihui Liang;Ming J. Zuo;Zhipeng Feng;Zhipeng Feng
Zhipeng Feng;Ming Liang;Yi Zhang;Shumin Hou
Zhipeng Feng;Xiaowang Chen;Ming Liang
Zhipeng Feng;Ming J. Zuo;Ming J. Zuo
Zhipeng Feng;Ming Liang
Zhipeng Feng;Dong Zhang;Ming J. Zuo
Zhipeng Feng;Xiaowang Chen;Tianyang Wang
Zhipeng Feng;Yakai Zhou;Ming J. Zuo;Ming J. Zuo;Fulei Chu
Zhipeng Feng;Sifeng Qin;Ming Liang
Zhiliang Liu;Yaqiang Jin;Ming J. Zuo;Ming J. Zuo;Zhipeng Feng
Zhipeng Feng;Ming J. Zuo;Ming J. Zuo;Jian Qu;Tao Tian
Zong Li;Zhipeng Feng;Fulei Chu
Zhipeng Feng;Fulei Chu;Ming J. Zuo
Zhipeng Feng;Wenying Zhu;Dong Zhang
Zhipeng Feng;Ming Liang
Zhipeng Feng;Ming Liang
Tianyang Wang;Qinkai Han;Fulei Chu;Zhipeng Feng
Xiaowang Chen;Zhipeng Feng
Zhipeng Feng;Xuefeng Lin;Ming J. Zuo;Ming J. Zuo
Zhipeng Feng;Zhipeng Feng;Fulei Chu
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