Z
Zhijian Wang
Researcher at North University of China
Publications - 23
Citations - 364
Zhijian Wang is an academic researcher from North University of China. The author has contributed to research in topics: Noise (signal processing) & Fault (power engineering). The author has an hindex of 10, co-authored 23 publications receiving 287 citations.
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Journal ArticleDOI
A Novel Fault Diagnosis Method of Gearbox Based on Maximum Kurtosis Spectral Entropy Deconvolution
TL;DR: The results of the simulation signal analysis show that the proposed MKSED method is superior to MED, and the proposed method is applied to bearing fault diagnosis, which verifies its ability to extract continuous impact.
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Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition
TL;DR: Permutation Entroy Optimization (PEO) is proposed in this paper and shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment.
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A Novel Method for Multi-Fault Feature Extraction of a Gearbox under Strong Background Noise
TL;DR: The diagnostic approach is formalized by extracting the multiple weak features with MOMEDA based on the MED denoised signals, which allows successful identification of multiple faults occurring simultaneously on the shaft and bearing in the high speed transmission stage of the gearbox.
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Weak Fault Diagnosis of Wind Turbine Gearboxes Based on MED-LMD
TL;DR: A new method for the extraction of multiple faults and weak features in strong background noise is provided and it is found that the failure of the wind power gearbox is generated from the micro-bending of the high-speed shaft and the pitting of the #10 bearing outer race at the output end of the highest speed shaft.
Journal ArticleDOI
A New Fuzzy Logic Classifier Based on Multiscale Permutation Entropy and Its Application in Bearing Fault Diagnosis
Wenhua Du,Xiaoming Guo,Zhijian Wang,Junyuan Wang,Mingrang Yu,Chuanjiang Li,Guanjun Wang,Longjuan Wang,Huaichao Guo,Jinjie Zhou,Yanjun Shao,Huiling Xue,Xingyan Yao +12 more
TL;DR: The multiscale permutation entropy (MPE), linear discriminant analysis (LDA), and the proposed HMDSOF was further used to classify the features and the results verify the superiority and generalization of the proposed fault diagnosis method.