Hao Chen
11 Papers
Hao Chen is an academic researcher. The author has contributed to research in topics: Computer science & Fault (geology). The author has an hindex of 1, co-authored 8 publications.
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Papers
Few-shot fault diagnosis of rotating machinery with two-branch prototypical networks
TL;DR: The results show that the TBPN model is suitable for fault diagnosis in the case of small data, and compared with using time domain signals or spectrum alone, their combination use can improve the effectiveness of fault diagnosis.
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Ordinal few-shot learning with applications to fault diagnosis of offshore wind turbines
TL;DR: Wang et al. as discussed by the authors applied the prototypical networks in few-short learning to cope with a small high-quality label data, and adopted the ordinal regression method to consider fault severity.
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Intelligent fault monitoring and diagnosis of tunnel fans using a hierarchical cascade forest.
TL;DR: Wang et al. as discussed by the authors presented a non-neural deep learning model, namely hierarchical cascade forest, which has three characteristics: (1) a hierarchical cascade structure is constructed, of which the output comes from each layer; (2) each fault class is evaluated and recognized independently, the result of fault classes that are easy to distinguish is output earlier; and (3) a confidence-based threshold estimate method is proposed in HCF and used to improve the training method to increase the reliability of HCF.
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A Novel Rotating Machinery Fault Diagnosis System Using Ensemble learning Capsule Autoencoder
TL;DR: An ensemble learning framework that integrates multiple stacked capsule autoencoders (SCAEs) for accurate fault diagnosis is proposed and a novel method for evaluating intrinsic templates based on a symmetric graph Laplacian with the aim of selecting capsules that can effectively reduce information redundancy is introduced.
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