Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noise

arXiv:2404.15341v1 Announce Type: cross
Abstract: Blind deconvolution (BD) has been demonstrated as an efficacious approach for extracting bearing fault-specific features from vibration signals under strong background noise. Despite BD's desirable feature in adaptability and mathematical interpretability, a significant challenge persists: How to effectively integrate BD with fault-diagnosing classifiers? This issue arises because the traditional BD method is solely designed for feature extraction with its own optimizer and objective function. When BD is combined with downstream deep learning classifiers, the different …

abstract adaptability arxiv blind challenge classifier cs.lg denoising diagnosis eess.sp feature features interpretability noise physics physics-informed type