首页|基于主成分分析的多域特征融合轴承故障诊断

基于主成分分析的多域特征融合轴承故障诊断

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针对复杂工况下难以区分轴承故障状态的问题,提出一种基于主成分分析的多域特征融合轴承故障诊断方法.采集轴承振动加速度信号,提取轴承时域新量纲一化特征、频域幅值谱特征和时频域经验模态分解特征共13维特征用于完整表征轴承状态;利用主成分分析方法对所提取特征融合与降维,降低诊断模型复杂度与数据分析难度;最后,选择合适的卷积神经网络进行分类,通过石化机组故障诊断实验平台进行验证.结果表明:多域融合特征相对于单域特征诊断效果更好,卷积神经网络分类模型相对于其他经典分类模型诊断准确率更高,融合诊断分类方法整体诊断准确率达到86%.
Bearing Fault Diagnosis Based on Principal Component Analysis and Multi-domain Feature Fusion
To solve the problem that it is difficult to distinguish bearing fault state under complex working conditions,a bearing fault diagnosis method of multi-domain feature fusion based on principal component analysis was proposed.The vibration acceleration signals was collected,and the new dimensionless features in time domain,amplitude spectrum features in frequency domain and empiri-cal mode decomposition features in time frequency domain were extracted to fully described the bearing state.The extracted features were fused and reduced in dimension by the principal component analysis method,it can effectively reduce the complexity of diagnostic mod-els and the difficulty of data analysis.Finally,a suitable convolutional neural network was selected to classify,the verification was per-formed by the petrochemical unit fault diagnosis experimental platform.The results show that the multi-domain fusion feature diagnosis is better than the single domain feature diagnosis,the convolutional neural network classification model has higher diagnostic accuracy than other classical classification models,the diagnostic accuracy of the fusion diagnosis classification method reaches 86%.

bearingfeature fusionprincipal component analysisconvolutional neural networkfault diagnosis

周凌孟、邓飞其、张清华、孙国玺、苏乃权、朱冠华

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华南理工大学自动化科学与工程学院,广东广州 510000

广东石油化工学院,广东省石化装备故障诊断重点实验室,广东茂名 525000

轴承 特征融合 主成分分析方法 卷积神经网络 故障诊断

国家自然科学基金重点项目广东省自然科学基金面上项目茂名市科技计划

61930004282022A1515010599170607111706145

2024

机床与液压
中国机械工程学会 广州机械科学研究院有限公司

机床与液压

CSTPCD北大核心
影响因子:0.32
ISSN:1001-3881
年,卷(期):2024.52(6)
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