Lu Jing

1.2k citations
12 papers · 967 · 2 hit papers · h-index 8

Impact in

Papers in

Lu Jing

11 papers receiving 946 citations

Lu Jing's Hit Papers

A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox 2017 · 566 citations
5660+3+6Years since publication100200300400500

Peers

Lu Jing
Comparison fields: 5 of 84
  • Control and Systems Engineering 663
  • Mechanical Engineering 477
  • Mechanics of Materials 265
  • Industrial and Manufacturing Engineering 80
  • Medical Laboratory Technology 8
Replace Meng Hee Lim with:
Meng Hee Lim Malaysia
Bram Vervisch Belgium
Yongzhi Qu United States
Purushottam Gangsar India
Fengjie Fan China
Gongbo Zhou China
Kun Xu China
Weiwei Qian China
Miao He United States
Lu Jing relative to Meng Hee Lim Malaysia Meng Hee Lim's profile →
Citations per field
00.5×2.9×
Meng Hee Lim · 1×
Citations per year

Countries citing papers authored by Lu Jing

Since Specialization
Citations

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

Fields of papers citing papers by Lu Jing

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 17 scholars most cited alongside Lu Jing, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Lu Jing Line = papers co-authored together Lu Jing links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1
A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox
Hit paper breakdown →
2017566
2
An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox
Hit paper breakdown →
2017310
3 201927
4 202218
5 202214
6 201311
7 20209
8 20209
9 20241
10 20161
11 20161
12 20160

About Lu Jing

Lu Jing is a scholar working on Control and Systems Engineering, Mechanical Engineering, Electrical and Electronic Engineering, Mechanics of Materials and Ocean Engineering, having authored 12 papers that have together received 967 indexed citations. Recurring topics across this work include Machine Fault Diagnosis Techniques (5 papers), Fault Detection and Control Systems (5 papers), Non-Destructive Testing Techniques (4 papers), Surface Roughness and Optical Measurements (2 papers), Industrial Vision Systems and Defect Detection (2 papers), Advanced Measurement and Detection Methods (2 papers), Geophysical Methods and Applications (2 papers) and Advanced Nanomaterials in Catalysis (1 paper). The work is most often cited by research in Control and Systems Engineering (663 citations), Mechanical Engineering (477 citations), Mechanics of Materials (265 citations), Industrial and Manufacturing Engineering (80 citations) and Medical Laboratory Technology (8 citations). Lu Jing has collaborated with scholars based in China and United States. Frequent co-authors include Ming Zhao, Li Pin, Xiaoqiang Xu, Peng Wang, Yiqing Zhang, Ling Zhao, Jialin Han, Zhiyan Guo, Chengjun Chen and Xiaorui Bai. Their work appears in journals such as Heliyon, Sensors, Materials Research Bulletin, Applied Sciences and Shock and Vibration.

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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