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This paper presents a novel method for predicting the remaining useful life (RUL) of acoustic bearings using adaptive dual-domain features and initial degradation point identification. The proposed approach enhances prediction accuracy by transforming acoustic emission signals into two-dimensional images and employing a two-dimensional grey-wavelet convolutional neural network for deeper feature extraction. Validation results indicate that this method significantly improves RUL prediction performance compar

Author

rishabhtyagi2k21phdce06

Language

EN