J
Jinglong Chen
Researcher at Xi'an Jiaotong University
Publications - 116
Citations - 3390
Jinglong Chen is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Computer science & Fault (power engineering). The author has an hindex of 17, co-authored 72 publications receiving 1767 citations. Previous affiliations of Jinglong Chen include University of Alberta.
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Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review
TL;DR: In this article, the inner product operation of wavelet transform (WT) is verified by simulation and field experiments and the development process of WT based on inner product is concluded and the applications of major developments in rotating machinery fault diagnosis are also summarized.
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Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals
TL;DR: In this paper, an empirical wavelet transform (EWT) is used to extract inherent modulation information by decomposing signal into mono-components under an orthogonal basis, which is seen as a powerful tool for mechanical fault diagnosis.
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Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive
TL;DR: In this paper, an independence-oriented VMD method via correlation analysis is proposed to adaptively extract weak and compound fault feature of wheel set bearing of high speed locomotive, and then the similar modes are combined according to the similarity of their envelopes to solve the over decomposition problem.
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Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process
TL;DR: The effectiveness of the proposed solution for RUL prediction of a nonlinear degradation process is proved by a case study of commercial modular aero-propulsion system simulation data (C-MAPSS-Data) from NASA.
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LiftingNet: A Novel Deep Learning Network With Layerwise Feature Learning From Noisy Mechanical Data for Fault Classification
TL;DR: Results show that the proposed method could achieve layerwise feature learning and successfully classify mechanical data even with different rotating speed and under the influence of random noise.