| Autor: |
Chen, Danmin, Zhang, Zhiqiang, Zhou, Funa, Wang, Chaoge |
| Předmět: |
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| Zdroj: |
Entropy; Dec2024, Vol. 26 Issue 12, p1007, 21p |
| Abstrakt: |
A convolutional neural network can extract features from high-dimensional data, but the convolution operation has a high time complexity and requires a large amount of computation. For equipment with a high sampling frequency, fault diagnosis methods based on convolutional neural networks cannot meet the requirements of online fault diagnosis. To solve this problem, this study proposes a fault diagnosis method for multi-source heterogeneous information fusion based on two-level transfer learning. This method aims to fully utilize multi-source heterogeneous information and external domain data, construct a two-level transfer mechanism to fuse multi-source heterogeneous information, avoid convolutional operations, and achieve real-time fault diagnosis. Its main work is to build a feature extraction network model of screenshots, design a mechanism for transfer from the feature extraction model using screenshots to the deep learning model using one-dimensional sequence signals, and complete the transfer from a convolutional neural network to a deep neural network. After two-level transfer, the fault diagnosis model not only integrates the characteristics of one-dimensional sequence signals and screenshots but also avoids convolution operations and has a low time complexity. The effectiveness of the proposed method is verified using a gearbox dataset and a bearing dataset. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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