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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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Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery
TL;DR: Results fully demonstrate that the stacked SAE-based diagnosis method can extract more discriminative high-level features and has a better performance in rotating machinery fault diagnosis compared with the traditional machine learning methods with shallow architectures.
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Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines
TL;DR: The experimental results show that the proposed method can not only solve the two deficiencies of SAEs, but also achieve a superior performance to the existing methods.
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Initial center frequency-guided VMD for fault diagnosis of rotating machines
TL;DR: The variation features of the center frequency (CF) of extracted modes are investigated with different ICFs, in which the converging U-shape phenomenon is found and a novel ICF-guided VMD method is proposed to extract accurately the weak damage features of rotating machines.
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A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines
TL;DR: A coarse-to-fine decomposing strategy is proposed for weak fault detection of rotating machines and can well-detect the weak repetitive transients in the signals with heavy noise and overcome the drawbacks of the original VMD.
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A novel bearing intelligent fault diagnosis framework under time-varying working conditions using recurrent neural network.
TL;DR: A new intelligent fault diagnosis framework inspired by the infinitesimal method is proposed that has higher accuracy with simpler structure, and is superior to the traditional method in bearing fault diagnosis.