World's Best Scientists 2026 revealed!

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
67
Citations
24143
World Ranking
451
National Ranking
63

Electronics and Electrical Engineering

D-Index
67
Citations
24164
World Ranking
1051
National Ranking
183

Overview

Jing Lin is affiliated with Beihang University in China and has a research portfolio rooted primarily in engineering, with a strong focus on control and systems engineering. Their work spans several interconnected subfields, including mechanical engineering, electrical and electronic engineering, mechanics of materials, and genetics.

The scientist's main areas of study concentrate on machine fault diagnosis techniques and fault detection and control systems. Additional topics of research include nutritional studies and diet, the impact of light on environment and health, gear and bearing dynamics analysis, engineering diagnostics and reliability, as well as anomaly detection techniques and applications.

Jing Lin has contributed extensively to a diverse array of scientific publications and research venues. Frequent publication outlets include:

  • SSRN Electronic Journal
  • IEEE Reliability Magazine
  • Reliability Engineering & System Safety
  • Applied Sciences
  • IEEE Internet of Things Journal

Collaborative efforts with various coauthors have been notable in their work. Frequent collaborators include Liangwei Zhang, Hongxi Yang, Lihui Zhou, Haidong Shao, and Yaogang Wang.

Among recent papers by Jing Lin are:

  • "A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance" (2021, Information Fusion)
  • "Restoration of smart grids: Current status, challenges, and opportunities" (2021, Renewable and Sustainable Energy Reviews)
  • "A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions" (2022, Engineering Applications of Artificial Intelligence)
  • "Designing Electronic Structures of Multiscale Helical Converters for Tailored Ultrabroad Electromagnetic Absorption" (2024, Nano-Micro Letters)
  • "Micro-helical Ni3Fe chain encapsulated in ultralight MXene/C aerogel to realize multi-functionality: Radar stealth, thermal insulation, fire resistance, and mechanical properties" (2024, Chemical Engineering Journal)

Best Publications

  • Machinery health prognostics: A systematic review from data acquisition to RUL prediction

    Yaguo Lei;Naipeng Li;Liang Guo;Ningbo Li

  • A review on empirical mode decomposition in fault diagnosis of rotating machinery

    Yaguo Lei;Jing Lin;Zhengjia He;Ming J. Zuo

  • Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

    Feng Jia;Yaguo Lei;Jing Lin;Xin Zhou

  • An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

    Yaguo Lei;Feng Jia;Jing Lin;Saibo Xing

  • A recurrent neural network based health indicator for remaining useful life prediction of bearings

    Liang Guo;Naipeng Li;Feng Jia;Yaguo Lei

  • Condition monitoring and fault diagnosis of planetary gearboxes: A review

    Yaguo Lei;Jing Lin;Ming J. Zuo;Ming J. Zuo;Zhengjia He

  • Broadband tristable energy harvester: Modeling and experiment verification

    Shengxi Zhou;Junyi Cao;Junyi Cao;Daniel J. Inman;Jing Lin

  • A Model-Based Method for Remaining Useful Life Prediction of Machinery

    Yaguo Lei;Naipeng Li;Szymon Gontarz;Jing Lin

  • An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings

    Naipeng Li;Yaguo Lei;Jing Lin;Steven X. Ding

  • Application of an improved kurtogram method for fault diagnosis of rolling element bearings

    Yaguo Lei;Jing Lin;Zhengjia He;Yanyang Zi

  • A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines

    Feng Jia;Yaguo Lei;Liang Guo;Jing Lin

  • A comprehensive review on convolutional neural network in machine fault diagnosis

    Jinyang Jiao;Ming Zhao;Jing Lin;Kaixuan Liang

  • Enhanced broadband piezoelectric energy harvesting using rotatable magnets

    Shengxi Zhou;Junyi Cao;Alper Erturk;Jing Lin

  • Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings

    Yonghao Miao;Ming Zhao;Ming Zhao;Jing Lin;Yaguo Lei

  • Planetary gearbox fault diagnosis using an adaptive stochastic resonance method

    Yaguo Lei;Yaguo Lei;Dong Han;Jing Lin;Zhengjia He

  • Magnetic-spring based energy harvesting from human motions: Design, modeling and experiments

    Wei Wang;Junyi Cao;Nan Zhang;Jing Lin

  • A New Method Based on Stochastic Process Models for Machine Remaining Useful Life Prediction

    Yaguo Lei;Naipeng Li;Jing Lin

  • An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis

    Zijian Qiao;Yaguo Lei;Jing Lin;Feng Jia

  • Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification

    Yonghao Miao;Ming Zhao;Ming Zhao;Jing Lin

  • Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition.

    Yonghao Miao;Ming Zhao;Jing Lin

  • Influence of potential well depth on nonlinear tristable energy harvesting

    Junyi Cao;Shengxi Zhou;Wei Wang;Jing Lin

  • Impact-induced high-energy orbits of nonlinear energy harvesters

    Shengxi Zhou;Shengxi Zhou;Junyi Cao;Daniel J. Inman;Shengsheng Liu

  • Residual joint adaptation adversarial network for intelligent transfer fault diagnosis

    Jinyang Jiao;Ming Zhao;Jing Lin;Kaixuan Liang

  • A tacho-less order tracking technique for large speed variations

    Ming Zhao;Jing Lin;Xiufeng Wang;Yaguo Lei

  • Enhanced mathematical modeling of the displacement amplification ratio for piezoelectric compliant mechanisms

    Mingxiang Ling;Mingxiang Ling;Junyi Cao;Minghua Zeng;Jing Lin

Frequent Co-Authors

Ming Zhao
Ming Zhao Xi'an Jiaotong University
Yaguo Lei
Yaguo Lei Xi'an Jiaotong University
Junyi Cao
Junyi Cao Xi'an Jiaotong University
Daniel J. Inman
Daniel J. Inman University of Michigan–Ann Arbor
Zhengjia He
Zhengjia He Xi'an Jiaotong University
Ming J. Zuo
Ming J. Zuo University of Alberta
Wei-Hsin Liao
Wei-Hsin Liao Chinese University of Hong Kong
Christopher R. Bowen
Christopher R. Bowen University of Bath
Jennifer E. Michaels
Jennifer E. Michaels Georgia Institute of Technology
Steven X. Ding
Steven X. Ding University of Duisburg-Essen

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

Exploring related fields can broaden your career opportunities beyond Mechanical and Aerospace Engineering. For example, students interested in helping others might consider different types of counselors, which offer diverse specialization choices tailored to personal interests and industry demands. Understanding these options is crucial for selecting the path that best fits your goals.

If you are looking for flexibility, some programs specialize in providing the easiest to get counseling degree, perfect for those seeking accessible entry points into this vital profession without compromising quality.

Additionally, those interested in psychology and behavior might find the fastest online masters in applied behavior analysis appealing. These accelerated programs allow professionals to expand their expertise while maintaining full-time careers, highlighting how online education adapts to busy lifestyles.

For individuals considering speech-language pathology, knowing how to get into SLP grad school requirements and acceptance rate is key. Navigating admission criteria and acceptance statistics helps set realistic expectations and prepare a competitive application.

By understanding these related degrees and pathways, students can make informed decisions on their educational journeys, enhancing their potential for success in competitive STEM and health-related fields.

Best Scientists Citing Jing Lin

Trending Scientists

Recently Published Articles