Complexity 2019:1-17 (2019)
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Abstract |
In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.
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DOI | 10.1155/2019/6943234 |
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References found in this work BETA
Application of an Improved Ensemble Local Mean Decomposition Method for Gearbox Composite Fault Diagnosis.Zhijian Wang, Junyuan Wang, Wenan Cai, Jie Zhou, Wenhua Du, Jingtai Wang, Gaofeng He & Huihui He - 2019 - Complexity 2019:1-17.
Citations of this work BETA
Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine.Zhijian Wang, Likang Zheng, Junyuan Wang & Wenhua Du - 2019 - Complexity 2019:1-19.
Dynamic Characteristics of an Offshore Wind Turbine with Tripod Suction Buckets Via Full-Scale Testing.Yun-Ho Seo, Moo Sung Ryu & Ki-Yong Oh - 2020 - Complexity 2020:1-16.
Deep Adaptive Adversarial Network-Based Method for Mechanical Fault Diagnosis Under Different Working Conditions.Jinrui Wang, Shanshan Ji, Baokun Han, Huaiqian Bao & Xingxing Jiang - 2020 - Complexity 2020:1-11.
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