多子阵声信号融合下轴向柱塞泵故障智能识别方法  

Fault intelligent identification method for axial piston pump using multi-subarray acoustical signal

作　　者：王长林 孙俊杰 钟永腾 WANG Changlin;SUN Junjie;ZHONG Yongteng(School of Information Technology,Jiangsu Open University,Nanjing 210003,CHN;School of Mechanical and Electrical Engineering,Wenzhou University,Wenzhou 325035,CHN)

机构地区：[1]江苏开放大学信息工程学院,江苏南京210003 [2]温州大学机电工程学院,浙江温州325035

出　　处：《制造技术与机床》2024年第9期23-28,共6页Manufacturing Technology & Machine Tool

基　　金：国家自然科学基金青年科学基金资助项目(51505339);浙江省自然科学基金青年基金资助项目(LQ16E050005)。

摘　　要：利用非接触式声阵列构造了多个子阵,建立了轴向柱塞泵故障噪声信号监测模型,并基于卷积神经网络-支持向量机(convolutional neural network-support vector machine,CNN-SVM)组合模型提出了故障智能识别方法。首先,运用子阵列平移的信号模型进行信号滤波,结合小波变换(continuous wavelet transform,CWT)生成时频图样本,通过多子阵合成RGB图片作为故障声信号样本;其次,用SVM替代Softmax分类器,建立了基于CNN-SVM的多子阵声信号融合的故障故障识别模型;最后,设计了柱塞故障、配流盘故障、斜盘故障和回程盘故障等4种故障并进行了实验验证。结果表明,所提方法在运行噪声环境下的分类准确率达到了97.5%,相较与单通道时频样本,其准确率提高了1.1%。A fault noise signal monitoring model of axial piston pump is established by using non-contact acoustic array,and an intelligent fault identification method is proposed based on CNN-SVM model.Firstly,the signal model of subarray translation is used to filter the signal,and the time-frequency graph sample is generated by continuous wavelet transform(CWT).RGB picture is synthesized by multiple subarrays as the fault acoustic signal sample.Secondly,the fault identification model of multi-subarray acoustic signal fusion based on CNN-SVM is established using SVM replaced Softmax classifier.Finally,four kinds of faults such as plunger fault,plate fault,swash plate fault and return plate fault are designed and verified by experiments.The results show that the classification accuracy of the proposed method reaches 97.5%in the running noise environment,which is 1.1%higher than that of the single channel time-frequency sample.

关 键 词：声信号 小波变换 RGB CNN-SVM模型 故障识别 

分 类 号：TP23[自动化与计算机技术—检测技术与自动化装置]