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无量纲与SVM的石化机组旋转机械故障诊断方法

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针对石化机组旋转机械故障信息存在非线性、重叠性等特点,提出一种无量纲与支持向量机(Support Vector Machine,SVM)的石化机组旋转机械故障诊断方法.首先对采集的振动信号进行分析并将其无量纲化;接着通过特征选择选取高价值与敏感性强的无量纲特征,降低分类模型复杂度并提高算法速度;最后通过选取合适的SVM分类模型进行分类诊断.结合具有无量纲特征的故障敏感性与SVM的非线性分类性进行诊断分类,并通过石化机组故障诊断实验平台进行验证,表明该方法相比于其他经典分类方法分类效果更好,分类正确率为99.1%,证明了方法的有效性.
Dimensionless and SVM Fault Diagnosis Method for Rotating Machinery of Petrochemical Units
In view of the nonlinear and overlapping characteristics of the fault information of the rotating machinery of petrochemical units,a fault diagnosis method based on the dimensionless and support vector machine(SVM)is proposed.Firstly,the collected vibration signals are analyzed and non-dimensionalized.Then,through feature selection,the dimensionless features with high value and strong sensitivity are selected to reduce the complexity of classification model and improve the speed of algorithm.Finally,the appropriate SVM classification model is selected for classification diagnosis.The fault sensitivity of dimensionless features and the nonlinear classification of SVM are combined for diagnosis and classification.Through the verification by the petrochemical unit fault diagnosis experimental platform,this fault diagnosis method is proved to have a better classification effect than the other classical classification methods,and the classification accuracy can reach 99.1%.

fault diagnosisrotating machinerydimensionlessfeature selectionSVM

周凌孟、张清华、邓飞其、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

故障诊断 旋转机械 无量纲特征 特征选择 SVM

国家自然科学基金重点资助项目国家自然科学基金重点资助项目国家自然科学基金重点资助项目广东省自然科学基金面上资助项目茂名市科技计划资助项目茂名市科技计划资助项目茂名市科技计划资助项目博士启动基金资助项目

6193301361673127619730942022A151501059920173042020S0041706071117061452020bs006

2024

10.3969/j.issn.1006-1355.2024.01.019

噪声与振动控制
中国声学学会

噪声与振动控制

CSTPCD北大核心
影响因子：0.622
ISSN：1006-1355
年,卷(期)：2024.44(1)
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