The comparison of blind deconvolution (BD) methods reveals that classifiers like WDCNN can significantly enhance fault diagnosis, outperforming traditional unsupervised methods.
Our findings demonstrate that integrating classifiers with blind deconvolution provides superior results, as supervised learning captures class-aware information that improves performance across various datasets.
#blind-deconvolution #fault-diagnosis #machine-learning #feature-extraction #computational-experiments
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