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Engineering and Technology

D-Index
46
Citations
7622
World Ranking
5193
National Ranking
1470

Overview

David He is affiliated with the University of Illinois at Chicago in the United States. Their research primarily focuses on engineering, with significant contributions in control and systems engineering, mechanical engineering, mechanics of materials, civil and structural engineering, and ocean engineering.

Their research topics include:

  • Machine Fault Diagnosis Techniques
  • Gear and Bearing Dynamics Analysis
  • Fault Detection and Control Systems
  • Engineering Diagnostics and Reliability
  • Mechanical Failure Analysis and Simulation
  • Structural Health Monitoring Techniques
  • Iterative Learning Control Systems

David He has published extensively in several venues, notably:

  • International Journal of Prognostics and Health Management
  • Annual Conference of the PHM Society
  • Journal of Intelligent Manufacturing
  • PHM Society Asia-Pacific Conference
  • IADC/SPE International Drilling Conference and Exhibition

Some recent papers demonstrate the breadth of David He's research:

  • Gear pitting fault diagnosis with mixed operating conditions based on adaptive 1D separable convolution with residual connection, 2020, Mechanical Systems and Signal Processing
  • Lubrication Oil Condition Monitoring and Remaining Useful Life Prediction with Particle Filtering, 2020, International Journal of Prognostics and Health Management
  • Unsupervised rotating machinery fault diagnosis method based on integrated SAE-DBN and a binary processor, 2020, Journal of Intelligent Manufacturing
  • Dynamic characteristics and reliability analysis of ball screw feed system on a lathe, 2020, Mechanism and Machine Theory
  • Time-dependent nonlinear dynamic model for linear guideway with crowning, 2020, Tribology International

Frequent co-authors collaborating with David He include:

  • Eric Bechhoefer
  • Yongzhi Qu
  • Xueyi Li
  • Jialin Li
  • Miao He

The collective work of David He reflects a focus on fault diagnosis, reliability analysis, and advanced control strategies within mechanical systems. Their contributions span numerous areas within engineering diagnostics and mechanical system performance monitoring.

Best Publications

  • Deep Learning Based Approach for Bearing Fault Diagnosis

    Miao He;David He

  • Using Deep Learning-Based Approach to Predict Remaining Useful Life of Rotating Components

    Jason Deutsch;David He

  • A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology

    Ming Dong;David He

  • Hidden semi-Markov model-based methodology for multi-sensor equipment health diagnosis and prognosis

    Ming Dong;David He

  • A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

    Jialin Li;Xueyi Li;David He

  • Rotational Machine Health Monitoring and Fault Detection Using EMD-Based Acoustic Emission Feature Quantification

    Ruoyu Li;D. He

  • PM2.5 concentration prediction using hidden semi-Markov model-based times series data mining

    Ming Dong;Dong Yang;Yan Kuang;David He

  • Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning

    Unknown

  • Gearbox tooth cut fault diagnostics using acoustic emission and vibration sensors--a comparative study.

    Yongzhi Qu;David He;Jae Yoon;Brandon Van Hecke

  • Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach

    D. He;Ruoyu Li;Junda Zhu

  • Design of assembly systems for modular products

    D.W. He;A. Kusiak

  • Low speed bearing fault diagnosis using acoustic emission sensors

    Brandon Van Hecke;Jae Yoon;David He

  • Lithium-ion battery life prognostic health management system using particle filtering framework

    M Dalal;J Ma;D He

  • Design of double- and triple-sampling X-bar control charts using genetic algorithms

    D. He;A. Grigoryan;M. Sigh

  • Equipment health diagnosis and prognosis using hidden semi-Markov models

    Ming Dong;David He;Prashant Banerjee;Jonathan Keller

  • Lubrication Oil Condition Monitoring and Remaining Useful Life Prediction with Particle Filtering

    Junda Zhu;Jae M. Yoon;David He;Yongzhi Qu

  • Online particle-contaminated lubrication oil condition monitoring and remaining useful life prediction for wind turbines

    Junda Zhu;Jae M. Yoon;David He;Eric Bechhoefer

  • Fault features extraction for bearing prognostics

    Ruoyu Li;Ponrit Sopon;David He

  • Analysis of sequential failures for assessment of reliability and safety of manufacturing systems

    Angela Adamyan;David He

  • A new hybrid deep signal processing approach for bearing fault diagnosis using vibration signals

    Miao He;David He

  • A survey of lubrication oil condition monitoring, diagnostics and prognostics techniques and systems

    Junda Zhu;David He;Eric Bechhoefer

  • Design for agile assembly: An operational perspective

    Andrew Kusiak;D. W. He

Frequent Co-Authors

Andrew Kusiak
Andrew Kusiak University of Iowa
Abhinav Saxena
Abhinav Saxena General Electric (United States)
Zude Zhou
Zude Zhou Wuhan University of Technology

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