基于改进延伸奇异值分解包的滚动轴承故障诊断
Improved Extended Singular Value Decomposition Packet and Its Application in Fault Diagnosis of Rolling Bearings
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摘要: 针对延伸奇异值分解包算法中依靠经验设定参数的问题, 提出了一种可自适应设定参数的改进延伸奇异值分解包算法。该方法利用信号的关键函数对其频谱趋势进行估计, 以此实现原算法中2个重要参数——分解精度和分解层数的自适应设定。引入了时域负熵指标, 在实现信号自适应分解的同时, 可对分解分量进行降噪处理, 提高分量的信噪比, 为之后的特征提取和故障诊断奠定基础。仿真信号和实验结果均表明该方法能有效地提取振动信号中的故障特征, 实现滚动轴承的故障诊断。Abstract: Aiming at the problem of parameter setting in the process of extended singular value decomposition packet, an improved method was proposed to select parameters adaptively. The spectrum trend was estimated by calculating the key function of the signal, to realize the adaptive setting of two important parameters in the original method: decomposition accuracy and decomposition layer. Besides, the time-domain negative entropy index was introduced to reduce the noise in the decomposed components, and to improve the signal-to-noise ratio of the components, which lays the foundation for the later feature extraction and fault diagnosis. The simulation and experimental results show that the proposed method can effectively extract the fault features of vibration signal and realize the fault diagnosis of rolling bearings.