Search Results for author: Guijun Ma

Found 4 papers, 1 papers with code

A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series

no code implementations1 Apr 2019 Wenqian Jiang, Cheng Cheng, Beitong Zhou, Guijun Ma, Ye Yuan

This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases.

Decoder Time Series +1

Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis

no code implementations2 Mar 2019 Cheng Cheng, Beitong Zhou, Guijun Ma, Dongrui Wu, Ye Yuan

However, for diverse working conditions in the industry, deep learning suffers two difficulties: one is that the well-defined (source domain) and new (target domain) datasets are with different feature distributions; another one is the fact that insufficient or no labelled data in target domain significantly reduce the accuracy of fault diagnosis.

Transfer Learning

A General End-to-end Diagnosis Framework for Manufacturing Systems

no code implementations17 Dec 2018 Ye Yuan, Guijun Ma, Cheng Cheng, Beitong Zhou, Huan Zhao, Hai-Tao Zhang, Han Ding

A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications.

Management

A deep learning-based remaining useful life prediction approach for bearings

1 code implementation8 Dec 2018 Cheng Cheng, Guijun Ma, Yong Zhang, Mingyang Sun, Fei Teng, Han Ding, Ye Yuan

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs).

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