A multi-scale collaborative fusion residual neural network-based approach for bearing fault diagnosis
Chen Qian,
Jun Gao,
Xing Shao
et al.
Abstract:In recent years, deep learning techniques have become popular for diagnosing equipment faults. However, their real industrial application performance is hindered by challenges related to noise and variable load conditions that prevent accurate extraction of valid feature information. To tackle these challenges, this paper proposed a novel approach known as the Multi-Scale Collaborative Fusion Residual Neural Network (MCFRNN) for bearing fault diagnosis. To begin with, the methodology introduces a Multi-Scale S… Show more
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