Journal Article10.1016/J.MEASUREMENT.2020.107741
Data synthesis using dual discriminator conditional generative adversarial networks for imbalanced fault diagnosis of rolling bearings
100
TL;DR: A novel framework named dual discriminator conditional generative adversarial networks (D2CGANs) is proposed to learn from sensor signals on multimodal fault samples and automatically synthesize realistic one-dimensional signals of each fault to meet requirement of online fault diagnosis.
read more
About: This article is published in Measurement. The article was published on 01 Jul 2020. The article focuses on the topics: Generative model & Condition monitoring.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
TL;DR: Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can not only leverage the advantages of Deep Learning (DL) in feature representation, but also benefit from the superiority of transfer learning (TL) in knowledge transfer as mentioned in this paper .
Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions.
None Fitri S. Kasim,Tianci Zhang,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.
340
Data synthesis using deep feature enhanced generative adversarial networks for rolling bearing imbalanced fault diagnosis
TL;DR: A novel data synthesis method called deep feature enhanced generative adversarial network is proposed to improve the performance of im balanced fault diagnosis and outperforms other intelligent methods and shows great potential in imbalanced fault diagnosis.
171
A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
Ieva Barauskienė,Weihua Li,Ruyi Huang,Jipu Li,Yixiao Liao,Zhuyun Chen,Guolin He,Ruqiang Yan,Konstantinos Gryllias +8 more
TL;DR: Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can not only leverage the advantages of Deep Learning (DL) in feature representation, but also benefit from the superiority of transfer learning (TL) in knowledge transfer.
161
A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations
Mohammed Hakim,Abdoulhdi Amhmad Borhana Omran,Ali Najah Ahmed,Muhannad Al-Waily,Abdallah Abdellatif +4 more
TL;DR: In this article , a comprehensive overview of deep learning based on bearing fault diagnosis is presented, which discusses a variety of transfer learning architectures and relevant theories while summarises, classifies, and explains several publications on the subject.
128
References
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
- 08 Dec 2014
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
SMOTE: synthetic minority over-sampling technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
SMOTE: Synthetic Minority Over-sampling Technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Deep learning and its applications to machine health monitoring
TL;DR: The applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder and its variants, Restricted Boltzmann Machines, Convolutional Neural Networks, and Recurrent Neural Networks.
2.2K
Cost-sensitive boosting for classification of imbalanced data
TL;DR: Three cost-sensitive boosting algorithms are developed by introducing cost items into the learning framework of AdaBoost, which show that one of the proposed algorithms tallies with the stagewise additive modelling in statistics to minimize the cost exponential loss.
1.5K