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)和奇异值分解技术的滚动轴承故障诊断方法。该方法首先采用EMD方法将滚动轴承振动信号分解为多个平稳的内禀分量(IntrinsicModefunction,简称IMF)之和,并形成初始特征向量矩阵。然后对初始特征向量矩阵进行奇异值分解得到矩阵的奇异值,将其作为滚动轴承振动信号的故障特征向量,并输入神经网络来识别滚动轴承的工作状态和故障类型。实验分析结果表明,本文方法能有效地应用于滚动轴承故障诊断。
According to the non-stationary characteristics of vibration signals from fault roller bearing a fault diagnosis approach for roller bearings based on EMD (empirical mode decomposition)method and singular value decomposition technique is proposed. The EMD method is used to decompose the vibration signal of a roller bearing into a number of IMF (intrinsic mode function) components from which the initial feature vector matrix is formed. By applying the singular value decomposition technique to the initial feature vector matrix, the decomposed singular values serve as the fault characteristic vector and are input into the neural network, and then the work condition and fault patterns are identified by the output of the neural network. The experimental results show that the proposed approach can be applied to the roller bearing fault diagnosis effectively.
出处
《振动与冲击》
EI
CSCD
北大核心
2005年第2期12-15,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
关键词
EMD
滚动轴承
奇异值分解
神经网络
Applications
Decomposition
Failure analysis
Neural networks
Vectors
Vibrations (mechanical)
