摘要
为了准确检测故障类型,综合运用时延自相关降噪和局部特征尺度分解方法,对齿轮的振动信号进行故障检测;对传感器测到的齿轮箱子振动信号进行自相关函数运算,将[-20,20]的低时延区间、左右端点内20个数据间的高时延区间内的自相关函数值置零以抑制噪声,得到时延自相关降噪信号;对该信号运用局部特征尺度分解方法进行分解,得到多个单分量信号,依据包含啮合频率准则选取有效分量;对有效分量应用包络分析技术进行故障检测;通过齿轮断齿故障振动试验数据的分析,发现该方法能明显抑制噪声,信噪比增益可达8.0963 dB,能够在故障信息不明显的情况下检测出齿轮箱故障与否,若存在故障则检测故障类型,能够有效支撑故障诊断。
In order to accurately detect gear faults,a comprehensive delayed autocorrelation denoising and local characteristic-scale decomposition(LCD)method was proposed to detect the faults of gear vibration signals,which calculated the autocorrelation function on the vibration signal of gear box measured by the sensor,and the low delay time of[-20,20]and high delay time of last 20 points of the autocorrelation function were set to zeros.The noise was suppressed to obtain the delayed autocorrelation denoising signals,the LCD method was used to decompose the signals and obtain multiple single component signals,and the intrinsic scale components(ISC)included the mesh frequency were selected as the effective component.The fault was detected by the envelope analysis techniques to effective components.The analysis of vibration test data for the gear broken tooth fault shows that the method can significantly suppress noise,the signal to noise ratio(SNR)can be 8.0963 dB,the method can detect gearbox fault without obvious fault information,if present,it can also detect the type of the gear fault,the method can effectively support fault diagnosis.
作者
崔伟成
刘林密
杨诗寓
宗富强
CUI Weicheng;LIU Linmi;YANG Shiyu;ZONG Fuqiang(Naval Aeronautical University,Yantai 264001,China)
出处
《计算机测量与控制》
2023年第9期70-76,共7页
Computer Measurement &Control
关键词
自相关函数
局部特征尺度分解
有效分量
齿轮故障检测
autocorrelation function
local characteristic-scale decomposition
effective component
gear fault detect
