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Abstract Traditional intelligent fault diagnosis techniques often struggle with signal distortion in environments characterized by strong multi-source noise, where fault signals are easily submerged and feature extraction becomes extremely unreliable. Furthermore, intelligent diagnosis is difficult to achieve using traditional denoising techniques that require extensive prior expert experience.
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