摘要
针对滚动轴承故障振动信号的非平稳特征,提出了一种基于经验模态分解(EmpiricalModeDecomposition ,简称EMD)和神经网络的滚动轴承故障诊断方法。该方法首先对原始信号进行了经验模态分解,将其分解为多个平稳 的固有模态函数(IntrinsicModefunction,简称IMF)之和,再选取若干个包含主要故障信息的IMF分量进行进一步分析, 由于滚动轴承发生故障时,加速度振动信号各频带的能量会发生变化,因而可从各IMF分量中提取能量特征参数作为神 经网络的输入参数来识别滚动轴承的故障类型。对滚动轴承的正常状态、内圈故障和外圈故障信号的分析结果表明,以 EMD为预处理器提取各频带能量作为特征参数的神经网络诊断方法比以小波包分析为预处理器的神经网络诊断方法有 更高的故障识别率,可以准确、有效地识别滚动轴承的工作状态和故障类型。
Aiming at the non-stationary characteristics of roller bearing fault vibration signals, roller bearing fault diagnosis method based on Empirical Mode Decomposition (EMD) and neural networks is put forward. First of all, original signals are decomposed into a finite number of stationary Intrinsic Mode functions (IMFs), then a number of IMFs containing main fault information is selected for further analysis. The energy of acceleration vibration signal would vary in different frequency bands when bearing fault occurs, therefore energy feature parameter extracted from IMFs could be served as input parameter of neural networks to identify fault patterns of roller bearing. The analysis results from roller bearing signals with inner-race and out-race faults show that the approach of neural network diagnosis based on EMD-extracting energy parameter of different frequency bands as feature is superior to that based on wavelet packet decomposition and reconstruction and can identify roller bearing fault patterns accurately and effectively.
出处
《振动与冲击》
EI
CSCD
北大核心
2005年第1期85-88,共4页
Journal of Vibration and Shock
基金
国家自然科学基金(编号:50275050)
高等学科博士点专项科研基金(编号:20020532024)资助项目
