Local Attention Pointer Bearing Fault Diagnosis

In order to improve the recognition accuracy of bearing vibration signal feature extraction as much as possible on the premise of ensuring the lightweight of the overall structure of the model, this study adopts an adaptive multi-channel multi-layer ResNet, combines the features extracted by PCA and BiGRU with the main body of ResNet in parallel, and designs the network residual block and connection structure, so as to assign weights to the convolutional layer training and further improve the integrity of feature representation. Through the ablation test of the noise signal, the method has achieved an accuracy rate of 99% in the identification of the bearing vibration signal fault pattern, and the comparison shows that the performance is higher stable than that of the traditional fault identification method, which effectively improves the fault feature extraction ability of the bearing vibration

搜集汇总

背景与挑战

背景概述

该数据集专注于轴承故障诊断，通过自适应多通道多层ResNet结合PCA和BiGRU特征提取方法，在轻量化模型结构下实现了99%的故障识别准确率，性能优于传统方法，有效提升了轴承振动信号的特征提取能力。