H
Huijun Zou
Researcher at Shanghai Jiao Tong University
Publications - 6
Citations - 422
Huijun Zou is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Sample entropy & Support vector machine. The author has an hindex of 6, co-authored 6 publications receiving 375 citations.
Papers
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Journal ArticleDOI
Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference
TL;DR: Experimental results indicate that the proposed approach cannot only reliably discriminate among different fault categories, but identify the level of fault severity, so the approach has possibility for bearing incipient fault diagnosis.
Journal ArticleDOI
A comparative study on ApEn, SampEn and their fuzzy counterparts in a multiscale framework for feature extraction *
TL;DR: Experimental results demonstrated that all four multiscale entropies outperformed single-scale ones, whilstMultiscale FSampEn was superior to other multiscales methods, especially when analyzed signals were contaminated by heavy noise.
Journal ArticleDOI
Applying improved multi-scale entropy and support vector machines for bearing health condition identification
TL;DR: In this study, IMSE and SVMs are employed as fault feature extractor and classifier, respectively, and the experimental results verify that the proposed method has potential applications in bearing health condition identification.
Book ChapterDOI
An Intelligent Fault Diagnosis Method Based on Multiscale Entropy and SVMs
TL;DR: A new method, named multiscale entropy (MSE), taking into account multiple time scales, was introduced for feature extraction from fault vibration signal and constitutes the proposed intelligent fault diagnosis method.
Journal ArticleDOI
Fault diagnosis based on optimized node entropy using lifting wavelet packet transform and genetic algorithms
TL;DR: In this article, a novel scheme for bearing fault diagnosis based on lifting wavelet packet transform (LWPT), sample entropy (SampEn), support vector machines (SVMs), and genetic algorithm was presented.