基于转速跟踪预处理和CNN的水力测功器轴承故障诊断方法  

A Fault Diagnosis Method of Hydraulic Dynamometer Bearings Based on Speed Tracking Preprocessing and CNN

作　　者：欧阳映辉 张槿 袁蓉 OUYANG Yinghui;ZHANG Jin;YUAN Rong(AECC Hunan Aviation Powerplant Research Institute,Zhuzhou Hunan 412002,China)

机构地区：[1]中国航发湖南动力机械研究所,湖南株洲412002

出　　处：《湖南工业大学学报》2024年第6期79-85,共7页Journal of Hunan University of Technology

摘　　要：高速、重载的复杂环境中,易导致水力测功器轴承故障多发,为准确诊断其轴承故障状态的问题,提出了一种基于转速跟踪处理和卷积神经网络(CNN)的水力测功器轴承故障诊断方法。首先,对振动数据进行转速跟踪预处理,以解决变转速情况下的特征提取问题;然后,对预处理后的信号进行快速傅里叶变化提取故障频谱特征,以频域信号生成汉克矩阵,并将其作为模型的原始特征输入训练卷积神经网络故障诊断模型;最后,采用训练好的轴承故障诊断模型实现水力测功器轴承故障在线诊断。仿真结果表明,所提出的基于转速跟踪处理和卷积神经网络的水力测功器模型是可行和有效的,并具有较好的泛化能力,能够准确、有效地诊断轴承的故障状态。A fault diagnosis method has been proposed for hydraulic dynamometer bearings based on speed tracking processing and convolutional neural network(CNN)for an accurate diagnosis of the bearing fault status caused by frequent bearing failures in complex environments with high speed and heavy load.Firstly,speed tracking preprocessing is performed on the vibration data in view of a solution of feature extraction under variable speed conditions.Next,the preprocessed signal is subjected to fast Fourier transform for an extraction of fault spectrum features,with the frequency domain signal used to generate a Hank matrix,which is subsequently used as the original feature input to train a convolutional neural network fault diagnosis model.Finally,the trained bearing fault diagnosis model is used to achieve an online diagnosis of hydraulic dynamometer bearing faults.The simulation results show that the proposed hydraulic dynamometer model based on speed tracking processing and convolutional neural network is characterized with an improved generalization ability,feasibility and effectiveness,which can accurately and effectively diagnose the fault status of bearings.

关 键 词：水力测功器 轴承故障诊断 转速跟踪 卷积神经网络(CNN) 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TK730.7[自动化与计算机技术—控制科学与工程]