E
Enyong Xu
Researcher at Huazhong University of Science and Technology
Publications - 20
Citations - 270
Enyong Xu is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 3, co-authored 6 publications receiving 23 citations.
Papers
More filters
Journal ArticleDOI
Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions.
Tianci Zhang,None Fitri S. Kasim,Jinglong Chen,Fudong Li,Kaiyu Zhang,Haixin Lv,Shuilong He,Enyong Xu +7 more
TL;DR: In this paper, a review of the research results on intelligent fault diagnosis with small and imbalanced data (S&I-IFD) is presented, which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification.
Journal ArticleDOI
Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis.
TL;DR: In this paper, a semi-supervised meta-learning network (SSMN) with squeeze-and-excitation attention is proposed for few-shot fault diagnosis, which consists of a parameterized encoder, a non-parameterized prototype refinement process and a distance function.
Journal ArticleDOI
Similarity-based meta-learning network with adversarial domain adaptation for cross-domain fault identification
Yong Feng,Jinglong Chen,Zhuozheng Yang,Xiaogang Song,Yuanhong Chang,Shuilong He,Enyong Xu,Zitong Zhou +7 more
TL;DR: Wang et al. as discussed by the authors proposed a domain-adversarial similarity-based meta-learning network (DASMN) consisting of three modules: a feature encoder, a classifier and a domain discriminator.
Journal ArticleDOI
Differentiable neural architecture search augmented with pruning and multi-objective optimization for time-efficient intelligent fault diagnosis of machinery
TL;DR: Experiments show that this novel differentiable neural architecture search method can generate subnetworks of lower complexity and less computational cost than other state-of-art Neural architecture search techniques, while achieving competitive result.
Journal ArticleDOI
Intelligent fault diagnosis under small sample size conditions via Bidirectional InfoMax GAN with unsupervised representation learning
TL;DR: Wang et al. as discussed by the authors proposed an unsupervised representation learning method called Bidirectional InfoMax GAN (BIMGAN), which can perform fast and effective feature extraction and fault recognition with few samples.