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基于双阶段信号融合和深度多层次多传感器网络的滚动轴承故障诊断

Zuozhou Pan¹, Yang Guan², Fengjie Fan²

¹College of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, PR China; College of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore.

ISA transactions

|September 17, 2024

概括

这项研究引入了一种新的轴承故障诊断方法,使用双阶段信号融合和深度多尺度网络. 该方法增强弱点,减少噪音,在复杂环境中实现更高的诊断准确性.

科学领域:

机械工程机械工程
信号处理信号处理
人工智能的人工智能

背景情况:

准确的轴承故障诊断对于工业机械至关重要.
现有的方法难以处理多传感器数据和弱故障信号.

研究的目的:

开发一种高精度轴承故障诊断方法.
在多传感器环境中增强功能提取和降噪.

主要方法:

两个阶段的信号融合:加权的经验波形变换和随机权重算法.
深度多尺度残余网络,扩展卷积用于特征提取.
用金字塔理论进行分类的特征融合.

主要成果:

拟议的方法有效地增强了弱故障特征.
通过信号融合技术实现了显著的噪声降低.
实验验证表明,与现有方法相比,诊断准确度更高.

结论:

开发的方法为轴承故障诊断提供了强大的解决方案.
信号融合和深度学习网络的结合显示出显著的前景.
这种方法在多传感器检测环境中有效.

关键词:

深度多尺度残余网络深度多尺度残余网络错误诊断是一个错误的诊断.多传感器信号融合信号滚动轴承的滚动轴承 .两个阶段的信号融合信号.

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