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Titlebook: New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques; Advanced Machine Lea Guangrui Wen,Zihao Lei,Xin Huang B

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發(fā)表于 2025-3-30 10:38:17 | 只看該作者
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發(fā)表于 2025-3-30 16:18:14 | 只看該作者
Remaining Life Assessment of Rolling Bearing Based on Graph Neural Networkture extraction and deep learning to mine and characterize the structured information of the signals. (2) It is difficult for the existing deep learning methods to model data in non-Euclidean spaces. In order to solve the above problems, this chapter proposes a remaining useful life assessment metho
53#
發(fā)表于 2025-3-30 19:26:50 | 只看該作者
Intelligent Fault Diagnosis Method Based on Multi-source Data and Multi-feature Fusione convolution and fusion convolution blocks are used for deep feature extraction and fusion. Finally, a joint loss function is reconstructed under the framework of unsupervised learning, which considers the distribution differences of the features and the label information simultaneously. The experi
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