ArticleScientific reports2026
Research on few-shot fault diagnosis and feature extraction mechanism based on ADPCW-ELCNN.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Aiming at weak features masked by noise, disconnected parameter-feature adaptation, and unbalanced generalization-efficiency in rotating machinery few-shot fault diagnosis, an Adaptive Dual-Parameter Collaborative Wavelet Convolutional Neural Network is proposed. Different from existing frameworks relying on data augmentation, transfer learning, or blind structural optimization, it adopts a physics-informed, parameter-collaborative, feature-faithful paradigm. Core innovations include quantitative correlation between wavelet scale and convolution kernel length, global-local adaptive strategy, and cascaded nonlinear enhancement. Experimental results on bearing and gearbox datasets show the model has 9.19 K parameters and 0.02 × 10
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