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Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 829-839 doi: 10.1007/s11465-021-0652-4

Abstract: To address this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-basedUnsupervised autoencoder is firstly used to compress every monitoring signal into a low-dimensional vector

Keywords： imbalanced fault diagnosis     graph feature learning     rotating machinery     autoencoder    

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Representation learning via a semi-supervised stacked distance autoencoder for image classification Research Articles

Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7,   Pages 963-1118 doi: 10.1631/FITEE.1900116

Abstract: is an important application of deep learning. In a typical classification task, the classification accuracy is strongly related to the features that are extracted via deep learning methods. An is a special type of , often used for dimensionality reduction and feature extraction. The proposed method is based on the traditional , incorporating the “distance” information between samples from different categories. The model is called a semi-supervised distance . Each layer is first pre-trained in an unsupervised manner. In the subsequent supervised training, the optimized parameters are set as the initial values. To obtain more suitable features, we use a stacked model to replace the basic structure with a single hidden layer. A series of experiments are carried out to test the performance of different models on several datasets, including the MNIST dataset, street view house numbers (SVHN) dataset, German traffic sign recognition benchmark (GTSRB), and CIFAR-10 dataset. The proposed semi-supervised distance method is compared with the traditional , sparse , and supervised . Experimental results verify the effectiveness of the proposed model.

Keywords： 自动编码器；图像分类；半监督学习；神经网络    

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Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder Article

Zong-feng QI, Qiao-qiao LIU, Jun WANG, Jian-xun LI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 12,   Pages 1991-2000 doi: 10.1631/FITEE.1601395

Abstract: To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder (KL-SAE) is proposed

Keywords： Battle damage assessment     Improved Kullback-Leibler divergence sparse autoencoder     Structural optimization    

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Title Author Date Type Operation

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Journal Article

Representation learning via a semi-supervised stacked distance autoencoder for image classification

Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn

Journal Article

Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder

Zong-feng QI, Qiao-qiao LIU, Jun WANG, Jian-xun LI

Journal Article