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Experiments on production and road-tested e-transaxle units show that both approaches provide interpretable diagnostic information, while SWIF achieves the most favorable balance between defect detection and false-positive control.
Recently, physics-informed learning-based intelligent bearing fault diagnosis methods have demonstrated great potential in improving diagnostic accuracy and physical consistency. Nevertheless, the practical deployment of conventional physics-informed neural networks typically relies on explicit partial differential equ...
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