The content discusses the limitations of neural networks in mechanical fault diagnosis due to poor interpretability. It introduces the PMN model that combines prototype-matching with autoencoder to improve understanding of fault signals. Experimental results demonstrate competitive diagnostic performance and enhanced representation learning capabilities of PMN.
Neural networks are widely used in intelligent fault diagnosis but lack interpretability, leading to challenges in practical applications. The proposed PMN integrates prototype-matching and autoencoder to enhance interpretability and provide insights into classification logic and typical fault signals. Conventional diagnosis experiments validate the effectiveness of PMN in improving diagnostic accuracy and representation learning.
Prototype-matching is introduced as a solution to enhance interpretability in mechanical fault diagnosis using neural networks. The PMN model combines human-inherent prototype-matching logic with an autoencoder for better understanding of classification logic and typical fault signals. Experimental results show promising outcomes in diagnostic accuracy and representation learning.
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by Qian Chen,Xi... a las arxiv.org 03-13-2024
https://arxiv.org/pdf/2403.07033.pdfConsultas más profundas