📫 学术镜像
📫 WOS
📫 Scopus
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Physics-informed Interpretable Wavelet Weight Initialization and Balanced Dynamic Adaptive Threshold for Intelligent Fault Diagnosis of Rolling Bearings
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IDSN: A one-stage Interpretable and Differentiable STFT domain adaptation Network for traction motor of high-speed trains cross-machine diagnosis
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Interpretable Physics-informed Domain Adaptation Paradigm for Cross-machine Transfer Diagnosis
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Interpretable modulated differentiable STFT and physics-informed balanced spectrum metric for freight train wheelset bearing cross-machine transfer fault diagnosis under speed fluctuations
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Journals of Prognostics and Health Management(智能故障诊断和寿命预测期刊)
- Fault diagnosis for small samples based on attention mechanism
- 基于Laplace小波卷积和BiGRU的少量样本故障诊断方法
Rolling Bearing Sub-Health Recognition via Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworks
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A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor Data and Periodic Sampling.
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Deep discriminative transfer learning network for cross-machine fault diagnosis.
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GTFE-Net: A Gramian Time Frequency Enhancement CNN for bearing fault diagnosis.
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A fault diagnosis method for rotating machinery based on CNN with mixed information.
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CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis.
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Maximum mean square discrepancy: A new discrepancy representation metric for mechanical fault transfer diagnosis.
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A blind deconvolution algorithm based on backward automatic differentiation and its application to rolling bearing fault diagnosis.
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Variance discrepancy representation: A vibration characteristic-guided distribution alignment metric for fault transfer diagnosis.
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