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使用ResNet50和AlexNet50算法对深槽球轴承进行故障分析

Vedant Jaiswal¹, Narendiranath Babu T², Pandiyan Murugan¹

¹School of Mechanical Engineering, Vellore Institute of Technology (VIT), Vellore, 632 014, India.

Scientific reports

|April 15, 2025

概括

这项研究使用14个特征和人工神经网络确定了四种类型的深槽球轴承 (DGBB) 故障. 在分类这些轴承故障方面,Resnet50算法实现了高97.9%的准确性.

科学领域:

机械工程机械工程
人工智能的人工智能
预测性维护是指预测性维护.

背景情况:

深槽球轴承 (DGBB) 是面临轴向和辐射负荷的关键工业部件.
轴承故障是一个重要的风险因素,影响机器的性能和安全.
现有的故障检测方法需要强大的分类策略.

研究的目的:

将四种不同类型的深球轴承故障分类为:外形故障 (CF),球形故障 (BF),内环故障 (IRF) 和外环故障 (ORF).
评估人工神经网络 (ANN) 对于自动轴承故障分类的有效性.
识别有助于准确故障诊断的最重要的特征.

主要方法:

利用了14个输入功能来进行轴承故障评估.
实施了特征排名方法,以确定每个参数的贡献.
使用人工神经网络 (ANN),包括Resnet50,用于自动故障分类.
训练并比较各种算法,评估预测概率.

主要成果:

使用Resnet50算法实现了97.9%的高分类准确度.
神经网络分类器学习器的准确率达到了97%.
观察到正确预测的概率随着故障样本数量的增加而下降.

结论:

人工神经网络,特别是Resnet50,对于准确的深槽球轴承故障分类非常有效.
功能排名为影响故障诊断的参数提供了有价值的见解.
开发的方法为工业应用中的预测性维护提供了一个有希望的方法.

关键词:

人工神经网络的人工神经网络球轴承球轴承球轴承深度神经网络是一个神经网络.错误分类错误分类是指错误的分类.机器学习是机器学习.支持矢量机器的支持矢量机器.

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