Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram
Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram
复制标题
使用多尺度聚类灰色信息图提取重复瞬态以进行旋转机械诊断
DOI：
10.1016/j.ymssp.2016.02.064
复制
发表时间：
2016-08-01
影响因子：
8.4
通讯作者：
Zurita, Grover
中科院分区：
文献类型：
--
作者：
Li, Chuan;Cabrera, Diego;Zurita, Grover
旋转机械的局部故障通常导致重复瞬态，其脉冲性或循环平稳性可作为故障特征。然而，在旋转机械诊断中，如何同时适应冲动性和循环平稳性是一项具有挑战性的任务。受近年来报道的利用时域或频域谱负熵对冲动性或循环平稳性敏感的信息图的启发，采用多尺度聚类方法将两种负熵以灰色方式结合起来，提出了一种多尺度聚类灰色信息图。将振动信号的傅立叶谱分解成不同初始分辨率的多个尺度。在每个尺度中，使用分层聚类对细段进行分组。同时，同时考虑时域和频域的谱负熵，通过对谱负熵的灰色评价来指导聚类。通过数值模拟和实验验证了所提出的MCGI。为了比较，采用对等方法来挑战不同的噪声和干扰。结果表明，基于多尺度谱聚类和两种负熵的灰色评价，MCGI在提取重复瞬态信号用于旋转机械诊断方面具有较强的鲁棒性。(C) 2016 Elsevier Ltd.版权所有。
Local faults of rotating machinery usually result in repetitive transients whose impulsiveness or cyclostationarity can be employed as faulty signatures. However, to simultaneously accommodate the impulsiveness and the cyclostationarity is a challenging task for rotating machinery diagnostics. Inspired by recently-reported infogram that is sensitive to either the impulsiveness or the cyclostationarity using spectral negentropy defined in time domain or frequency domain, a multiscale clustering grey infogram (MCGI) is proposed by combining both negentropies in a grey fashion using multiscale clustering. Fourier spectrum of the vibration signal is decomposed into multiple scales with different initial resolutions. In each scale, fine segments are grouped using hierarchical clustering. Meanwhile, both time-domain and frequency-domain spectral negentropies are taken into account to guide the clustering through grey evaluation of both negentropies. Numerical simulations and experimental tests are carried out for validating the proposed MCGI. For comparison, peer methods are applied to challenge different noises and interferences. The results show that, thanks to the multiscale clustering of the spectrum and the grey evaluation of both negentropies, the present MCGI is robust in extracting the repetitive transients for the rotating machinery diagnosis. (C) 2016 Elsevier Ltd. All rights reserved.