Maximum second-order cyclostationarity blind deconvolution (CYCBD) is accomplished by maximizing the second-order cyclostationarity of signals through the indicator of second-order cyclostationarity (ICS2). It is a significant method for extracting weak periodic pulses related to bearing faults. However, since the interference spectral lines generated by the interference signals in squared envelope spectrum do not be considered by ICS2, the method can be invalid for certain applications. In addition, when the detected signal contains compound faults, only one fault can be enhanced, and misdiagnosis is easily caused. This article proposes cyclostationarity blind deconvolution via eigenvector screening, abbreviated as ESCYCBD, for fault feature enhancement and extraction of rotating machinery in order to solve these problems. Different from existing deconvolution methods which use the maximum value of the index as a criterion to select the filter coefficients, this method proposes the concept of eigenvector subspace, which contains the optimal eigenvector as filter coefficients that are ignored by CYCBD. Subsequently, the Fourier coefficients related to a series of cyclic frequencies are extended for compound fault signals. Besides, by using the harmonic characteristics of fault features in the envelope spectrum, fault feature recognition and optimal selection are carried out in eigenvector subspaces. At the same time, the concept of signals set is created, by which means different feature modes of different fault can be extracted at one time. The analysis results of simulation signals and experimental data show that the proposed ESCYCBD has robustness and accuracy in extracting fault feature modes, whether it is single fault diagnosis or compound fault diagnosis.

中文翻译：

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基于特征向量筛选的循环平稳盲反褶积及其在旋转机械状态监测中的应用


最大二阶循环平稳盲反卷积（CYCBD）是通过二阶循环平稳性指标（ICS2）最大化信号的二阶循环平稳性来实现的。它是提取与轴承故障相关的微弱周期性脉冲的重要方法。然而，由于ICS2没有考虑平方包络谱中干扰信号产生的干扰谱线，因此该方法对于某些应用可能无效。另外，当检测到的信号包含复合故障时，只能增强一种故障，容易造成误诊。本文提出通过特征向量筛选的循环平稳盲反卷积（ESCYCBD），用于旋转机械的故障特征增强和提取，以解决这些问题。与现有反卷积方法以索引最大值作为选择滤波器系数的标准不同，该方法提出了特征向量子空间的概念，其中包含最优特征向量作为被CYCBD忽略的滤波器系数。随后，将与一系列循环频率相关的傅里​​叶系数扩展为复合故障信号。此外，利用故障特征在包络谱中的谐波特性，在特征向量子空间中进行故障特征识别和优化选择。同时创建了信号集的概念，可以一次性提取出不同故障的不同特征模式。仿真信号和实验数据的分析结果表明，无论是单一故障诊断还是复合故障诊断，所提出的ESCYCBD在提取故障特征模式方面都具有鲁棒性和准确性。