基于SAMNV3的滚动轴承智能故障诊断方法
Intelligent Fault Diagnosis Method for Rolling Bearing Based on SAMNV3
张兰昕 1向玲 1李显泽 1陈锦鹏1
作者信息
- 1. 华北电力大学机械工程系 河北保定 071003
- 折叠
摘要
滚动轴承是机械设备的关键部件,为了对滚动轴承的故障类别进行有效识别,提出了一种融合自注意力机制(Self-At-tention,SA)和轻量级网络MobileNetV3的SAMNV3滚动轴承智能故障诊断模型.利用该模型中自注意力机制对特征进行自适应加权的优点以及轻量级网络MobileNetV3体积较小的优点,通过直接将两个不同数据集的原始振动信号输入SAMNV3模型中,进行故障的特征提取与识别分类,从而实现端到端的滚动轴承智能故障诊断.在两种不同的数据集上进行验证,结果表明该模型具有较高的准确率和较低的计算复杂度,可以有效提高滚动轴承故障诊断的准确性和可靠性.
Abstract
In order to accurately identify the fault categories of rolling bearings,which are essential components of mechanical equipment,this paper proposes a SAMNV3 intelligent fault diagnosis model for rolling bearings that integrates the self-attention(SA)mechanism and the lightweight network MobileNetV3.This model takes advantage of the adaptive weighting of the features by the self-attention mechanism and the small size of the lightweight network MobileNetV3 to achieve end-to-end rolling bearing intelligent fault diagnosis by directly inputting the original vibration signals from two different datasets into the SAMNV3 model for feature extraction and fault identification and classification.The results of the validation of the two different datasets show that the model has high accuracy and low computational complexity,which can effectively improve the accuracy and reliability of rolling bearing fault diagnosis.
关键词
滚动轴承/智能故障诊断/自注意力机制/轻量级网络/MobileNetV3Key words
Rolling bearing/Intelligent fault diagnosis/Self-attention mechanism/Lightweight network/MobileNetV3引用本文复制引用
基金项目
国家自然科学基金(52075170)
国家自然科学基金(52175092)
出版年
2024
NETL
NSTL
万方数据