(1DCNN 一维深度卷积神经网络)的轴承故障诊断(python代码)

上传者: wq6qeg88 | 上传时间: 2022-05-03 19:03:48 | 文件大小: 3.63MB | 文件类型: ZIP
一维DCNN用于轴承故障诊断,仿真数据集为CWRU(凯西私储大学的公开轴承数据集) 轴承故障诊断时机械状态监测的热门研究方向,其算法的核心在于信号特征提取与模式分类两个部分。在轴承故障诊断领域,常见的特征提取算法有快速傅里叶变化,小波变换,经验模式分解以及信号的统计学特征等,常见的模式分类算法有支持向量机,BP 神经网络(也称为多层感知器),贝叶斯分类器以及最近邻分类器等。当下轴承故障诊断的研究热点是可以归结为 3 类:寻找更好的特征表达;寻找最适合的特征表达以及分类器的组合;以及发明新的传感器。

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