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🌱 My published paper：

1. :fire::trophy:Physics-informed Interpretable Wavelet Weight Initialization and Balanced Dynamic Adaptive Threshold for Intelligent Fault Diagnosis of Rolling Bearings`

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2. IDSN: A one-stage Interpretable and Differentiable STFT domain adaptation Network for traction motor of high-speed trains cross-machine diagnosis

   • (Coming soon!)
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3. :fire::trophy:Interpretable Physics-informed Domain Adaptation Paradigm for Cross-machine Transfer Diagnosis

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4. :fire::trophy: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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5. Prior knowledge-embedded first-layer interpretable paradigm for rail transit vehicle human-computer collaboration fault monitoring

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6. Journals of Prognostics and Health Management（智能故障诊断和寿命预测期刊）

7. Awesome large language model for Prognostics and health management（用于预测性维护与健康管理的大型语言模型）

8. Awesome Multi-modal datasets for fault diagnosis（多模态故障诊断数据集）

​ $\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow$Publications during the master 's degree$\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow\Downarrow$

1. :trophy:Fault diagnosis for small samples based on attention mechanism
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2. 基于Laplace小波卷积和BiGRU的少量样本故障诊断方法
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3. ~~Rolling Bearing Sub-Health Recognition via Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworks~~
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💬 Published papers that I try to reproduce（unofficial code）

1. :trophy:Deep Residual Shrinkage Networks for Fault Diagnosis.

2. A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor Data and Periodic Sampling.

3. :fire::trophy:Deep discriminative transfer learning network for cross-machine fault diagnosis.

4. :trophy:GTFE-Net: A Gramian Time Frequency Enhancement CNN for bearing fault diagnosis.

5. Capsule network for fault diagnosis.

6. Milling chatter recognition.

7. A fault diagnosis method for rotating machinery based on CNN with mixed information.

8. :fire::trophy:CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis.

9. :trophy:Maximum mean square discrepancy: A new discrepancy representation metric for mechanical fault transfer diagnosis.

10. A blind deconvolution algorithm based on backward automatic differentiation and its application to rolling bearing fault diagnosis.

11. xLSTM for Intelligent Fault Diagnosis of Rolling Bearings.

12. :trophy:Variance discrepancy representation: A vibration characteristic-guided distribution alignment metric for fault transfer diagnosis.

13. DG-Softmax: A new domain generalization intelligent fault diagnosis method for planetary gearboxes.

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• chaohe#bjtu.edu.cn
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