Research on Fault Diagnosis of Rolling Bearing Based on Gramian Angular Field and Lightweight Model
Jingtao Shen,
Zhe Wu,
Yachao Cao
et al.
Abstract:Due to the limitations of deep learning models in processing one-dimensional signal feature extraction, and high model complexity leading to low training accuracy and large consumption of computing resources, this paper innovatively proposes a rolling bearing fault diagnosis method based on Gramian Angular Field (GAF) and enhanced lightweight residual network. Firstly, the one-dimensional signal is transformed into a two-dimensional GAF image, fully preserving the signal’s temporal dependency. Secondly, to add… Show more
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