World's Best Scientists 2026 revealed!

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
51
Citations
8983
World Ranking
1124
National Ranking
138

Minping Jia 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 Minping Jia 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: 156 publications — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Minping Jia 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 Minping Jia 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: 51 D-Index — 69th percentile

69% of scientists in this discipline score the same or lower.

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

Overview

Minping Jia is affiliated with Southeast University in China and has a significant body of work in the field of Engineering. Their research spans several specialized subfields including Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials, Artificial Intelligence, and Radiological and Ultrasound Technology.

The scientist has contributed extensively to topics related to machine fault diagnosis and mechanical system reliability. The main research themes covered in their publications include:

  • Machine Fault Diagnosis Techniques
  • Gear and Bearing Dynamics Analysis
  • Engineering Diagnostics and Reliability
  • Fault Detection and Control Systems
  • Non-Destructive Testing Techniques
  • Occupational Health and Safety Research
  • Mechanical Failure Analysis and Simulation

Frequent publication venues for Minping Jia's work include:

  • Reliability Engineering & System Safety
  • IEEE Transactions on Instrumentation and Measurement
  • Measurement
  • Mechanical Systems and Signal Processing
  • IEEE/ASME Transactions on Mechatronics

They have authored several notable papers, including:

  • "A novel time-frequency Transformer based on self-attention mechanism and its application in fault diagnosis of rolling bearings" (2021) in Mechanical Systems and Signal Processing
  • "A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings" (2021) in Reliability Engineering & System Safety
  • "Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings" (2021) in Reliability Engineering & System Safety
  • "A BiGRU method for remaining useful life prediction of machinery" (2020) in Measurement
  • "Intelligent Fault Diagnosis of Gearbox Under Variable Working Conditions With Adaptive Intraclass and Interclass Convolutional Neural Network" (2022) in IEEE Transactions on Neural Networks and Learning Systems

Minping Jia has collaborated extensively with several co-authors, with multiple joint publications alongside:

  • Yifei Ding
  • Xiaoan Yan
  • Yudong Cao
  • Peng Ding
  • Xiaoli Zhao

Best Publications

  • A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings

    Unknown

  • A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing

    Xiaoan Yan;Minping Jia

  • Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mRMR feature selection

    Unknown

  • A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings

    Yudong Cao;Yifei Ding;Minping Jia;Rushuai Tian

  • Intelligent Fault Diagnosis of Gearbox Under Variable Working Conditions With Adaptive Intraclass and Interclass Convolutional Neural Network

    Unknown

  • Application of CSA-VMD and optimal scale morphological slice bispectrum in enhancing outer race fault detection of rolling element bearings

    Unknown

  • Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings

    Yifei Ding;Jichao Zhuang;Peng Ding;Minping Jia

  • A BiGRU method for remaining useful life prediction of machinery

    Daoming She;Daoming She;Minping Jia

  • Engineering Applications of Intelligent Monitoring and Control 2014

    Qingsong Xu;Pak-Kin Wong;Minping Jia;Chengjin Zhang

  • DCC-CenterNet: A rapid detection method for steel surface defects

    Rushuai Tian;Minping Jia

  • Transfer learning for remaining useful life prediction of multi-conditions bearings based on bidirectional-GRU network

    Yudong Cao;Minping Jia;Peng Ding;Yifei Ding

  • Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum

    Xiaoan Yan;Minping Jia;Ling Xiang

  • Semisupervised Graph Convolution Deep Belief Network for Fault Diagnosis of Electormechanical System With Limited Labeled Data

    Xiaoli Zhao;Minping Jia;Zheng Liu

  • Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions

    Xiaoan Yan;Xiaoan Yan;Ying Liu;Minping Jia

  • A convolutional neural network-based method for workpiece surface defect detection

    Junjie Xing;Minping Jia

  • Normalized Conditional Variational Auto-Encoder with adaptive Focal loss for imbalanced fault diagnosis of Bearing-Rotor system

    Unknown

  • Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM

    Unknown

  • Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery

    Xiaoli Zhao;Minping Jia;Mingyao Lin

  • Remaining useful life estimation using deep metric transfer learning for kernel regression

    Yifei Ding;Minping Jia;Qiuhua Miao;Peng Huang

  • Multistep forecasting for diurnal wind speed based on hybrid deep learning model with improved singular spectrum decomposition

    Xiaoan Yan;Ying Liu;Yadong Xu;Minping Jia

  • Deep regularized variational autoencoder for intelligent fault diagnosis of rotor–bearing system within entire life-cycle process

    Xiaoan Yan;Daoming She;Yadong Xu;Minping Jia

  • Model Reference Adaptive Control With Perturbation Estimation for a Micropositioning System

    Qingsong Xu;Minping Jia

  • Multichannel fault diagnosis of wind turbine driving system using multivariate singular spectrum decomposition and improved Kolmogorov complexity

    Xiaoan Yan;Ying Liu;Yadong Xu;Minping Jia

  • A novel unsupervised deep learning network for intelligent fault diagnosis of rotating machinery

    Xiaoli Zhao;Minping Jia

  • Remaining Useful Life Estimation Under Multiple Operating Conditions via Deep Subdomain Adaptation

    Yifei Ding;Minping Jia;Yudong Cao

  • Intelligent Fault Diagnosis of Multichannel Motor–Rotor System Based on Multimanifold Deep Extreme Learning Machine

    Xiaoli Zhao;Minping Jia;Peng Ding;Chen Yang

  • Meta deep learning based rotating machinery health prognostics toward few-shot prognostics

    Peng Ding;Minping Jia;Xiaoli Zhao

Frequent Co-Authors

Xiaoli Zhao
Xiaoli Zhao Tongji University
Zheng Liu
Zheng Liu University of British Columbia
Michael Pecht
Michael Pecht University of Maryland, College Park

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