D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 64 Citations 13,964 182 World Ranking 473 National Ranking 45

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Mechanical engineering

Fault, Artificial intelligence, Hilbert–Huang transform, Pattern recognition and Wavelet are his primary areas of study. His work deals with themes such as Feature, Vibration, Noise, Signal processing and Feature extraction, which intersect with Fault. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Data mining, Machine tool and Machining.

His Hilbert–Huang transform study integrates concerns from other disciplines, such as Class, Speech recognition, Simulation and Demodulation. His study looks at the relationship between Pattern recognition and fields such as Bearing, as well as how they intersect with chemical problems. His study on Wavelet transform is often connected to Expression as part of broader study in Wavelet.

His most cited work include:

  • A review on empirical mode decomposition in fault diagnosis of rotating machinery (947 citations)
  • Condition monitoring and fault diagnosis of planetary gearboxes: A review (354 citations)
  • Application of the EEMD method to rotor fault diagnosis of rotating machinery (343 citations)

What are the main themes of his work throughout his whole career to date?

Zhengjia He spends much of his time researching Wavelet, Vibration, Fault, Algorithm and Finite element method. His study looks at the intersection of Wavelet and topics like Mathematical analysis with Daubechies wavelet and Modal analysis. The concepts of his Vibration study are interwoven with issues in Control theory, Condition monitoring, Structural engineering, Feature extraction and Bearing.

His Fault research is multidisciplinary, incorporating perspectives in Artificial intelligence, Hilbert–Huang transform, Signal, Electronic engineering and Pattern recognition. The Algorithm study combines topics in areas such as Feature, Fault detection and isolation, Mathematical optimization, Signal processing and Kurtosis. His Finite element method research includes elements of Applied mathematics and Rotor.

He most often published in these fields:

  • Wavelet (33.51%)
  • Vibration (30.37%)
  • Fault (28.80%)

What were the highlights of his more recent work (between 2013-2018)?

  • Vibration (30.37%)
  • Fault (28.80%)
  • Structural engineering (15.71%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Vibration, Fault, Structural engineering, Wavelet and Algorithm. His Vibration research incorporates themes from Redundancy, Energy and Principal component analysis. He has included themes like Electronic engineering, Control theory, Condition monitoring and Wavelet transform in his Fault study.

His Wavelet transform study combines topics in areas such as Hilbert–Huang transform, Feature extraction and Hilbert transform. His research integrates issues of Background noise and Mathematical analysis in his study of Wavelet. His Algorithm research integrates issues from Basis, Signal, Noise and Downtime.

Between 2013 and 2018, his most popular works were:

  • Condition monitoring and fault diagnosis of planetary gearboxes: A review (354 citations)
  • Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review (224 citations)
  • Matching Demodulation Transform and SynchroSqueezing in Time-Frequency Analysis (151 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Mechanical engineering
  • Artificial intelligence

Fault, Wavelet, Vibration, Wavelet transform and Structural engineering are his primary areas of study. His Fault research includes themes of Algorithm, Electronic engineering and Condition monitoring. Zhengjia He has researched Electronic engineering in several fields, including Feature extraction and Feature.

His Wavelet study combines topics from a wide range of disciplines, such as Finite element method, Numerical analysis, Noise and Arch. His Vibration research focuses on subjects like Bearing, which are linked to Control theory, Amplitude modulation, Translation and Topology. Zhengjia He works mostly in the field of Wavelet transform, limiting it down to concerns involving Fault detection and isolation and, occasionally, Rolling-element bearing.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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

Yaguo Lei;Jing Lin;Zhengjia He;Ming J. Zuo.
Mechanical Systems and Signal Processing (2013)

1255 Citations

Application of the EEMD method to rotor fault diagnosis of rotating machinery

Yaguo Lei;Zhengjia He;Yanyang Zi.
Mechanical Systems and Signal Processing (2009)

527 Citations

Condition monitoring and fault diagnosis of planetary gearboxes: A review

Yaguo Lei;Jing Lin;Ming J. Zuo;Ming J. Zuo;Zhengjia He.
Measurement (2014)

491 Citations

Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble

Qiao Hu;Zhengjia He;Zhousuo Zhang;Yanyang Zi.
Mechanical Systems and Signal Processing (2007)

440 Citations

Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs

Yaguo Lei;Zhengjia He;Yanyang Zi;Qiao Hu.
Mechanical Systems and Signal Processing (2007)

416 Citations

A new approach to intelligent fault diagnosis of rotating machinery

Yaguo Lei;Zhengjia He;Yanyang Zi.
Expert Systems With Applications (2008)

412 Citations

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

Yaguo Lei;Jing Lin;Zhengjia He;Yanyang Zi.
Mechanical Systems and Signal Processing (2011)

373 Citations

Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

Jinglong Chen;Zipeng Li;Jun Pan;Gaige Chen.
Mechanical Systems and Signal Processing (2016)

280 Citations

Application of an intelligent classification method to mechanical fault diagnosis

Yaguo Lei;Zhengjia He;Yanyang Zi.
Expert Systems With Applications (2009)

264 Citations

EEMD method and WNN for fault diagnosis of locomotive roller bearings

Yaguo Lei;Zhengjia He;Yanyang Zi.
Expert Systems With Applications (2011)

263 Citations

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