Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method
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TLDR
In this paper, a rolling bearing fault diagnosis model which combines Dual-stage Attention-based Recurrent Neural Network (DA-RNN) and Convolutional Block Attention Module (CBAM) is proposed.About:
This article is published in Measurement.The article was published on 2021-11-25 and is currently open access. It has received 34 citations till now. The article focuses on the topics: Fault (geology) & Convolutional neural network.read more
Citations
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Attention Mechanism in Intelligent Fault Diagnosis of Machinery: A Review of Technique and Application
TL;DR: In this paper , the relevant research and applications of Attention Mechanism in Intelligent Fault Diagnosis of Machinery are reviewed and classified into three categories: recurrent-based, convolution-based and self-attention-based.
Journal ArticleDOI
Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Afshin Shoeibi,Marjane Khodatars,Mahboobeh Jafari,Navid Ghassemi,Parisa Moridian,Roohallah Alizadesani,Sai Ho Ling,Abbas Khosravi,Hamid Alinejad-Rokny,Hak-Keung Lam,Matthew Fuller-Tyszkiewicz,U. Rajendra Acharya,D Anderson,Yudong Zhang,Juan Manuel Górriz +14 more
TL;DR: In this article , the authors present a comprehensive review of brain disease detection from the fusion of neuroimaging modalities using DL models like convolutional neural networks, recurrent neural networks (RNNs), pretrained, generative adversarial networks (GANs), and autoencoders (AEs).
Journal ArticleDOI
Data-augmented wavelet capsule generative adversarial network for rolling bearing fault diagnosis
TL;DR: In this article , a wavelet capsule generative adversarial network (WCGAN) is proposed to address the issue of rolling bearing fault diagnosis with limited imbalance data, which keeps convolutional neural networks (CNNs) shift invariant to extract the deep features of the data.
Journal ArticleDOI
A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors
TL;DR: In this article , the authors summarized the fault location, sensor types, bearing fault types, and fault signal analysis of rolling bearings and divided the fault signal types into one-dimensional and two-dimensional images.
Journal ArticleDOI
A class-aware supervised contrastive learning framework for imbalanced fault diagnosis
Jiyang Zhang,Jianxiao Zou,Zhiheng Su,Jianxiong Tang,Yuhao Kang,Hongbing Xu,Zhiliang Liu,Shi Cai Fan +7 more
TL;DR: Zhang et al. as discussed by the authors proposed a feature-learning-based method called Class-aware Supervised Contrastive Learning (CA-SupCon) to optimize the feature difference between any two classes by leveraging category information.
References
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Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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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.
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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.
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