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.
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.
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.
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.
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)
Application of the EEMD method to rotor fault diagnosis of rotating machinery
Yaguo Lei;Zhengjia He;Yanyang Zi.
Mechanical Systems and Signal Processing (2009)
Condition monitoring and fault diagnosis of planetary gearboxes: A review
Yaguo Lei;Jing Lin;Ming J. Zuo;Ming J. Zuo;Zhengjia He.
Measurement (2014)
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)
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)
A new approach to intelligent fault diagnosis of rotating machinery
Yaguo Lei;Zhengjia He;Yanyang Zi.
Expert Systems With Applications (2008)
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)
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)
Application of an intelligent classification method to mechanical fault diagnosis
Yaguo Lei;Zhengjia He;Yanyang Zi.
Expert Systems With Applications (2009)
EEMD method and WNN for fault diagnosis of locomotive roller bearings
Yaguo Lei;Zhengjia He;Yanyang Zi.
Expert Systems With Applications (2011)
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