首页|基于声信号的离心泵故障诊断研究

基于声信号的离心泵故障诊断研究

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各种原因使得工业现场设备状态监测的首选测量信号是声信号时,提出一种基于声信号的设备状态监测方法显得尤为必要.以某型离心泵为依据对象,对现场采集的声信号提取梅尔倒谱系数(MFCC)作为信号的初始特征,然后计算这些MFCC初始特征的散布熵(DE)值,并通过主成分分析法(PCA)对矩阵进行降维,从而构造特征矩阵.利用蝙蝠优化算法(BA)对支持向量机(SVM)的惩罚系数与核函数参数进行优化,对离心泵的多种故障工况开展诊断,并与多种诊断方法进行比较.实验结果表明,经过BA优化后的模型在诊断准确率上提高了21.7％;在该模型的基础上利用DE对MFCC提取的信号进行深度挖掘,使模型诊断的准确率提高2.05％.
Research on fault diagnosis of centrifugal pump based on acoustic signal
When the preferred measurement signal for equipment condition monitoring in industrial field is acoustic signal for various reasons,it is especially necessary to propose an equipment condition monitoring method based on acoustic signal. In this paper,a certain type of centrifugal pump is taken as the basis object,and the Mel-scale frequency cepstral coefficients(MFCC)are extracted from the acoustic signals collected in the field as the initial features of the signals,then the dispersion entropy(DE)values of these MFCC initial features are calculated,and the matrix is downscaled by principal component analysis(PCA),so as to construct the feature matrix. The penalty coefficients and kernel function parameters of the support vector machine(SVM)are optimized by using the bat algorithm(BA)to carry out diagnosis of various fault conditions of centrifugal pumps and compared with various diagnostic methods. The experimental results show that the model optimized by BA improves the diagnostic accuracy by 21.7％;on the basis of this model,the deep mining of the signals extracted by MFCC using DE improves the diagnostic accuracy of the model by 2.05％.

centrifugal pump fault diagnosisacoustic signalmel-scale frequency cepstral coefficients dispersion entropy(MFCCDE)bat optimization algorithmsupport vector machine

陈剑、姜涛、陈品

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合肥工业大学噪声振动工程研究所 合肥 230009

安徽省汽车NVH 技术研究中心 合肥 230009

离心泵故障诊断 声信号 梅尔倒谱散布熵 蝙蝠优化算法 支持向量机

国家自然科学基金青年基金安徽省科技重大专项

1160407017030901049

2024

10.13382/j.jemi.B2407260

电子测量与仪器学报
中国电子学会

电子测量与仪器学报

CSTPCD北大核心
影响因子：2.52
ISSN：1000-7105
年,卷(期)：2024.38(5)
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