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为在保障模型整体结构轻量化的前提下，尽可能提升轴承振动信号特征提取的识别精度，本研究采用自适应多通道多层残差网络（ResNet），将主成分分析（PCA）与双向门控循环单元（BiGRU）提取的特征与ResNet主体并行融合，并设计了网络残差块与连接结构，以此为卷积层训练分配权重，进一步完善特征表征的完整性。通过含噪信号的消融实验验证，该方法在轴承振动信号故障模式识别中准确率可达99%；对比实验表明，其性能相较于传统故障识别方法更为稳定，有效提升了轴承振动的故障特征提取能力

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。