A Homogeneous Stacking Ensemble Learning Model for Fault Diagnosis of Rotating Machinery With Small Samples-Reference-Cited by-同舟云学术

A Homogeneous Stacking Ensemble Learning Model for Fault Diagnosis of Rotating Machinery With Small Samples

Author:

Cao Zhi¹^ORCID,Li Zhenxiang²^ORCID,Zhang Junhua³,Fu Hongyong²^ORCID

Affiliation:

1. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

2. Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China

3. School of Electronic and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China

Funder

Key Laboratory of Space Utilization, Chinese Academy of Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

Reference38 articles.

1. Least squares generative adversarial networks;mao;arXiv 1611 04076,2016

2. Improved training of Wasserstein GANs;gulrajani;arXiv 1704 00028,2017

3. Wasserstein GAN;arjovsky;arXiv 1701 07875,2017

4. Conditional image synthesis with auxiliary classifier GANs;odena;arXiv 1610 09585,2016

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