Criterion fusion for spectral segmentation and its application to optimal demodulation of bearing vibration signals
Criterion fusion for spectral segmentation and its application to optimal demodulation of bearing vibration signals
复制标题
谱分割准则融合及其在轴承振动信号优化解调中的应用
DOI：
10.1016/j.ymssp.2015.04.004
复制
发表时间：
2015-12-01
影响因子：
8.4
通讯作者：
Wang, Tianyang
中科院分区：
文献类型：
--
作者：
Li, Chuan;Liang, Ming;Wang, Tianyang
通过对振动信号的共振解调，可以检测出有缺陷的轴承特征。轴承故障检测的决策在很大程度上取决于所识别的谐振频带的质量。定位谐振频带的两个关键问题是感兴趣的频谱的适当分割和用于引导搜索谐振频带的标准。针对这两个问题，本文提出了一种准则融合的方法来指导光谱分割过程。与所提出的方法，轴承信号的频谱首先被划分成初始的细段，然后自适应地合并到不同的子集，使用增强的自底向上分割技术。为了指导频谱分割和合并过程，三个常用的标准，即，使用基于熵的方法将峰度、平滑度指数和波峰因子融合到合成成本函数中。通过这种方法传递的最终频带具有谐振频带的良好覆盖，并且然后用于解调轴承信号。模拟和实验信号已被用来评估所提出的方法，这也被比较单准则的方法。结果表明，融合准则比单一准则具有更好的效果。(C)2015爱思唯尔有限公司版权所有。
The defective bearing signatures can be detected by resonance demodulation of the vibration signals. The decision of the bearing fault detection largely depends on the quality of the identified resonant frequency band. Two key issues in locating the resonance frequency band are the proper segmentation of the frequency spectrum of interest and the criterion used to guide the search for the resonance band. To deal with the two issues, this paper proposes a criterion fusion approach to guide the spectral segmentation process. With the proposed approach, the frequency spectrum of the bearing signal is first divided into initial fine segments which are then adaptively merged into different subsets using an enhanced bottom-up segmentation technique. To guide the spectral segmentation and merging process, three commonly used criteria, i.e., kurtosis, smoothness index and crest factor are fused into a synthesized cost function using an entropy-based method. The final frequency band delivered by this approach has a good coverage of the resonant band and is then used to demodulate bearing signals. Both simulated and experimental signals have been employed to evaluate the proposed approach, which has also been compared to single-criterion methods. The comparison indicates that the fused criterion yields better results than those from the single-criterion. (C) 2015 Elsevier Ltd. All rights reserved.