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Xingxing Jiang
Researcher at Soochow University (Suzhou)
Publications - 106
Citations - 2569
Xingxing Jiang is an academic researcher from Soochow University (Suzhou). The author has contributed to research in topics: Fault (power engineering) & Computer science. The author has an hindex of 19, co-authored 79 publications receiving 1275 citations. Previous affiliations of Xingxing Jiang include Nanjing University of Aeronautics and Astronautics.
Papers
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Journal ArticleDOI
Deep Adaptive Adversarial Network-Based Method for Mechanical Fault Diagnosis under Different Working Conditions
TL;DR: It is expected that the proposed DAAN can effectively learn domain-invariant features to bridge the discrepancy between the data from different working conditions.
Patent
Stacked SAE (Sparse Autoencoder) deep neural network-based bearing fault diagnosis method
TL;DR: In this article, a stacked SAE (Sparse Autoencoder) deep neural network-based bearing fault diagnosis method was proposed, where the first layer of a network is applied to the qualitative judgment of a bearing fault, and the second layer of the network is used to the quantitative judgment of the bearing fault.
Journal ArticleDOI
Transferable graph features-driven cross-domain rotating machinery fault diagnosis
TL;DR: Wang et al. as mentioned in this paper proposed a transferable graph features-driven cross-domain rotating machinery fault diagnosis approach, where graph data has been integrated into transfer learning-based cross domain rotating machinery diagnosis for reducing domain discrepancy.
Journal ArticleDOI
A Novel Data Augmentation Method for Intelligent Fault Diagnosis Under Speed Fluctuation Condition
TL;DR: Experimental results of gearbox and bearing datasets show that the DAESPN model has strong feasibility to carry out data augmentation for fault diagnosis of rotating machines under speed fluctuation condition.
Journal ArticleDOI
An intelligent fault diagnosis method of rotating machinery using L1-regularized sparse filtering
TL;DR: An improved sparse filtering with L1 regularization (L1SF) is proposed to improve the generalization ability by improving the sparsity of the weight matrix, which can extract more discriminative features.