World's Best Scientists 2026 revealed!
Siliang Lu

Siliang Lu

Award Badge
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
2557
National Ranking
310

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
Haidong Shao
Haidong Shao Hunan University
Changqing Shen
Changqing Shen Soochow University
Jérôme Antoni
Jérôme Antoni Institut National des Sciences Appliquées de Lyon
Wenping Cao
Wenping Cao Anhui University
Lifeng Xi
Lifeng Xi Shanghai Jiao Tong University
Ruqiang Yan
Ruqiang Yan Xi'an Jiaotong University
Clarence W. de Silva
Clarence W. de Silva University of British Columbia

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students interested in expanding their expertise beyond Mechanical and Aerospace Engineering, several related online degrees offer valuable interdisciplinary skills. For example, those looking to quickly enhance their credentials might explore the quickest online ABA masters program, which provides advanced knowledge in behavioral analysis applicable to technology-driven human interaction systems.

Additionally, the field of Speech-Language Pathology (SLP) presents an intriguing career pathway closely connected to engineering advancements in communication devices and assistive technology. Prospective students should consider important factors like admission criteria by reviewing SLP graduate programs to understand the requirements for enrollment.

Moreover, gaining insights on program accessibility is crucial. Identifying the speech-language pathology graduate programs by state can help applicants find the most suitable and attainable options depending on their location and background.

Cost is another key concern. To make an informed decision, students should carefully evaluate the speech pathology degree cost online to balance quality education with budget considerations.

Best Scientists Citing Siliang Lu

Trending Scientists

Recently Published Articles