World's Best Scientists 2026 revealed!
Siliang Lu

Siliang Lu

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Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
34
Citations
3798
World Ranking
881
National Ranking
286

Mechanical and Aerospace Engineering

D-Index
36
Citations
4602
World Ranking
2560
National Ranking
310

Siliang Lu publication distribution in Mechanical and Aerospace Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mechanical and Aerospace Engineering in 2026. The highlighted bar marks where Siliang Lu sits on this spectrum.

47–56 publications: 10 scientists 57–66 publications: 23 scientists 67–76 publications: 32 scientists 77–86 publications: 62 scientists 87–96 publications: 67 scientists 97–106 publications: 91 scientists 107–116 publications: 113 scientists 117–126 publications: 115 scientists 127–136 publications: 130 scientists 137–146 publications: 140 scientists 147–156 publications: 155 scientists 157–166 publications: 132 scientists 167–176 publications: 133 scientists 177–186 publications: 130 scientists 187–196 publications: 140 scientists 197–206 publications: 115 scientists 207–216 publications: 125 scientists 217–226 publications: 117 scientists 227–236 publications: 99 scientists 237–246 publications: 92 scientists 247–256 publications: 100 scientists 257–266 publications: 95 scientists 267–276 publications: 88 scientists 277–286 publications: 77 scientists 287–296 publications: 74 scientists 297–306 publications: 74 scientists 307–316 publications: 62 scientists 317–326 publications: 70 scientists 327–336 publications: 59 scientists 337–346 publications: 58 scientists 347–356 publications: 45 scientists 357–366 publications: 44 scientists 367–376 publications: 36 scientists 377–386 publications: 41 scientists 387–396 publications: 32 scientists 397–406 publications: 23 scientists 407–416 publications: 28 scientists 417–426 publications: 27 scientists 427–436 publications: 25 scientists 437–446 publications: 23 scientists 447–456 publications: 23 scientists 457–466 publications: 20 scientists 467–476 publications: 12 scientists 477–486 publications: 24 scientists 487–496 publications: 18 scientists 497–506 publications: 12 scientists 507–516 publications: 13 scientists 517–526 publications: 21 scientists 527–536 publications: 12 scientists 537–546 publications: 8 scientists 547–556 publications: 16 scientists 557–566 publications: 3 scientists 567–576 publications: 11 scientists 577–586 publications: 6 scientists 587–596 publications: 5 scientists 597–606 publications: 6 scientists 607–616 publications: 7 scientists 617–626 publications: 7 scientists 627–636 publications: 10 scientists 637–646 publications: 4 scientists 647–656 publications: 3 scientists 657–658 publications: 2 scientists 659+ publications: 100 scientists
47 publications 659+

This scientist: 117 publications — 12th percentile

12% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 659 publications or more.

Siliang Lu D-index placement in Mechanical and Aerospace Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Mechanical and Aerospace Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Siliang Lu sits on this spectrum.

30 D-Index: 83 scientists 31 D-Index: 113 scientists 32 D-Index: 144 scientists 33 D-Index: 153 scientists 34 D-Index: 189 scientists 35 D-Index: 158 scientists 36 D-Index: 139 scientists 37 D-Index: 127 scientists 38 D-Index: 130 scientists 39 D-Index: 126 scientists 40 D-Index: 104 scientists 41 D-Index: 100 scientists 42 D-Index: 107 scientists 43 D-Index: 101 scientists 44 D-Index: 103 scientists 45 D-Index: 79 scientists 46 D-Index: 88 scientists 47 D-Index: 70 scientists 48 D-Index: 83 scientists 49 D-Index: 44 scientists 50 D-Index: 64 scientists 51 D-Index: 56 scientists 52 D-Index: 50 scientists 53 D-Index: 48 scientists 54 D-Index: 58 scientists 55 D-Index: 52 scientists 56 D-Index: 48 scientists 57 D-Index: 42 scientists 58 D-Index: 34 scientists 59 D-Index: 42 scientists 60 D-Index: 37 scientists 61 D-Index: 42 scientists 62 D-Index: 44 scientists 63 D-Index: 22 scientists 64 D-Index: 33 scientists 65 D-Index: 29 scientists 66 D-Index: 23 scientists 67 D-Index: 29 scientists 68 D-Index: 24 scientists 69 D-Index: 19 scientists 70 D-Index: 34 scientists 71 D-Index: 26 scientists 72 D-Index: 19 scientists 73 D-Index: 18 scientists 74 D-Index: 19 scientists 75 D-Index: 14 scientists 76 D-Index: 19 scientists 77 D-Index: 8 scientists 78 D-Index: 18 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 17 scientists 82 D-Index: 11 scientists 83 D-Index: 16 scientists 84 D-Index: 7 scientists 85 D-Index: 9 scientists 86 D-Index: 8 scientists 87 D-Index: 6 scientists 88 D-Index: 6 scientists 89 D-Index: 7 scientists 90 D-Index: 10 scientists 91 D-Index: 4 scientists 92 D-Index: 4 scientists 93+ D-Index: 100 scientists
30 D-Index 93+

This scientist: 36 D-Index — 28th percentile

28% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 93 D-Index or more.

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Siliang Lu is affiliated with Anhui University in China and has contributed extensively to the field of engineering, particularly focusing on areas related to machine fault diagnosis and industrial systems. Their research spans multiple subfields including control and systems engineering, mechanical engineering, electrical and electronic engineering, civil and structural engineering, and industrial and manufacturing engineering.

The scientist's main research topics include:

  • Machine Fault Diagnosis Techniques
  • Non-Destructive Testing Techniques
  • Fault Detection and Control Systems
  • Gear and Bearing Dynamics Analysis
  • Industrial Vision Systems and Defect Detection
  • Structural Health Monitoring Techniques
  • Welding Techniques and Residual Stresses

Siliang Lu has published in several frequent venues, showing a focus on instrumentation and industrial informatics, as well as reliability and sensor technology. These venues include:

  • IEEE Transactions on Instrumentation and Measurement
  • IEEE Sensors Journal
  • IEEE Transactions on Industrial Informatics
  • Reliability Engineering & System Safety
  • IEEE Internet of Things Journal

The scientist's notable recent papers are:

  • Edge Computing on IoT for Machine Signal Processing and Fault Diagnosis: A Review, 2023, IEEE Internet of Things Journal
  • Highly Efficient Fault Diagnosis of Rotating Machinery Under Time-Varying Speeds Using LSISMM and Small Infrared Thermal Images, 2022, IEEE Transactions on Systems Man and Cybernetics Systems
  • Hidden Markov Model based Stochastic Resonance and its Application to Bearing Fault Diagnosis, 2022, Journal of Sound and Vibration
  • Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning, 2021, Reliability Engineering & System Safety
  • Efficient Data Reduction at the Edge of Industrial Internet of Things for PMSM Bearing Fault Diagnosis, 2021, IEEE Transactions on Instrumentation and Measurement

Frequent coauthors working with Siliang Lu reflect a collaboration network within their research areas. These include:

  • Xiaoxian Wang
  • Juncai Song
  • Yongbin Liu
  • Min Xia
  • Hui Wang

Best Publications

  • A review of stochastic resonance in rotating machine fault detection

    Siliang Lu;Qingbo He;Jun Wang

  • Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning

    Min Xia;Haidong Shao;Darren Williams;Siliang Lu

  • Highly Efficient Fault Diagnosis of Rotating Machinery Under Time-Varying Speeds Using LSISMM and Small Infrared Thermal Images

    Unknown

  • Edge Computing on IoT for Machine Signal Processing and Fault Diagnosis: A Review

    Unknown

  • Tacholess Speed Estimation in Order Tracking: A Review With Application to Rotating Machine Fault Diagnosis

    Siliang Lu;Ruqiang Yan;Yongbin Liu;Qunjing Wang

  • Effects of underdamped step-varying second-order stochastic resonance for weak signal detection

    Siliang Lu;Qingbo He;Fanrang Kong

  • Stochastic resonance with Woods-Saxon potential for rolling element bearing fault diagnosis

    Siliang Lu;Qingbo He;Fanrang Kong

  • Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals

    Siliang Lu;Xiaoxian Wang;Qingbo He;Fang Liu

  • Online Fault Diagnosis of Motor Bearing via Stochastic-Resonance-Based Adaptive Filter in an Embedded System

    Siliang Lu;Qingbo He;Tao Yuan;Fanrang Kong

  • Bearing fault diagnosis of a permanent magnet synchronous motor via a fast and online order analysis method in an embedded system

    Siliang Lu;Qingbo He;Jiwen Zhao

  • Condition monitoring and fault diagnosis of motor bearings using undersampled vibration signals from a wireless sensor network

    Siliang Lu;Peng Zhou;Xiaoxian Wang;Yongbin Liu

  • Rotating machine fault diagnosis through enhanced stochastic resonance by full-wave signal construction

    Siliang Lu;Qingbo He;Haibin Zhang;Fanrang Kong

  • Optimal design of permanent magnet linear synchronous motors based on Taguchi method

    Juncai Song;Fei Dong;Jiwen Zhao;Siliang Lu

  • Sound-aided vibration weak signal enhancement for bearing fault detection by using adaptive stochastic resonance

    Siliang Lu;Ping Zheng;Yongbin Liu;Zheng Cao

  • Enhanced Rotating Machine Fault Diagnosis Based on Time-Delayed Feedback Stochastic Resonance

    Siliang Lu;Qingbo He;Haibin Zhang;Fanrang Kong

  • Edge Computing: A Promising Framework for Real-Time Fault Diagnosis and Dynamic Control of Rotating Machines Using Multi-Sensor Data

    Gang Qian;Siliang Lu;Donghui Pan;Huasong Tang

  • A novel weak-fault detection technique for rolling element bearing based on vibrational resonance

    Lei Xiao;Xinghui Zhang;Siliang Lu;Tangbin Xia

  • Time-varying singular value decomposition for periodic transient identification in bearing fault diagnosis

    Shangbin Zhang;Siliang Lu;Qingbo He;Fanrang Kong

  • A New Methodology to Estimate the Rotating Phase of a BLDC Motor With Its Application in Variable-Speed Bearing Fault Diagnosis

    Siliang Lu;Xiaoxian Wang

  • Novel synthetic index-based adaptive stochastic resonance method and its application in bearing fault diagnosis

    Peng Zhou;Siliang Lu;Fang Liu;Yongbin Liu

  • Feature fusion using kernel joint approximate diagonalization of eigen-matrices for rolling bearing fault identification

    Yongbin Liu;Bing He;Fang Liu;Siliang Lu

  • A Novel Contactless Angular Resampling Method for Motor Bearing Fault Diagnosis Under Variable Speed

    Siliang Lu;Jie Guo;Qingbo He;Fang Liu

  • Stochastic resonance in an underdamped system with FitzHug-Nagumo potential for weak signal detection

    Cristian López;Wei Zhong;Siliang Lu;Feiyun Cong

  • Efficient Data Reduction at the Edge of Industrial Internet of Things for PMSM Bearing Fault Diagnosis

    Xiaoxian Wang;Siliang Lu;Wenbin Huang;Qunjing Wang

  • IoT-Based Signal Enhancement and Compression Method for Efficient Motor Bearing Fault Diagnosis

    Huasong Tang;Siliang Lu;Gang Qian;Jianming Ding

Frequent Co-Authors

Qingbo He
Qingbo He Shanghai Jiao Tong University
Fang Liu
Fang Liu Beihang University
Fanrang Kong
Fanrang Kong University of Science and Technology of China
Clarence W. de Silva
Clarence W. de Silva University of British Columbia
Ruqiang Yan
Ruqiang Yan Xi'an Jiaotong University
Lifeng Xi
Lifeng Xi Shanghai Jiao Tong University
Wenping Cao
Wenping Cao Anhui University
Jérôme Antoni
Jérôme Antoni Institut National des Sciences Appliquées de Lyon
Changqing Shen
Changqing Shen Soochow University
Haidong Shao
Haidong Shao Hunan University

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