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首页> 外文期刊>Mechanical systems and signal processing >Finding a frequency signature for a cyclostationary signal with applications to wheel bearing diagnostics
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Finding a frequency signature for a cyclostationary signal with applications to wheel bearing diagnostics

机译：查找轮转平稳信号的频率特征及其在车轮轴承诊断中的应用

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In recent years, an increasing number of researches in signal processing was dedicated to frequency identification and analysis of cyclostationarity. The survey by Gardner et al. (2006) [8] have quoted over 1500 different papers recently published that are dedicated to cyclostationarity. An important application of cyclostationary signals is the analysis of mechanical signals generated by a vibrating mechanism. In this area of research the paper by Antoni (2009) [2] shows the importance of cyclostationary models to perform basic operations on signals in the time and frequency domain. The result in this paper presented a new perspective on cyclostationary signal analysis and on frequency identification for such signals. One of the fundamental problems in diagnosis of rotating mechanism is in identification of significant modulating frequencies that contribute to the cyclostationary nature of the signals. So far, the statistical methods for frequency identification in cyclostationary signals were based either on the assumption of gaussianity of the signal and/or on the assumption of some linear structure of the signal. The recent research by Lenart et al. (2008) [13] has shown that there are modern tools available for analyzing cyclostationary signals and they are based on the idea of resampling of observed signals. The aim of this paper is to show applicability of a resampling technique called subsampling in frequency identification for cyclostationary signals. The theoretical results are accompanied with applications to frequency analysis of cyclostationary signal generated by a wheel bearings, one without any damage and the another two with two different types of faults. The result showed that the normal operating conditions and abnormal operating conditions for the bearings can be identified via resampling-based frequency analysis and subsequent frequency identification based on a statistical test.
机译：近年来，越来越多的信号处理研究致力于频率识别和循环平稳性分析。 Gardner等人的调查。 （2006）[8]引用了最近发表的关于循环平稳性的1500多种不同的论文。循环平稳信号的重要应用是对由振动机构产生的机械信号的分析。在这一研究领域，Antoni（2009）[2]的论文表明了循环平稳模型对时域和频域信号执行基本运算的重要性。本文的结果为循环平稳信号分析和此类信号的频率识别提供了新的视角。诊断旋转机构的基本问题之一是识别有助于信号的循环平稳特性的重要调制频率。到目前为止，用于循环平稳信号的频率识别的统计方法要么基于信号的高斯性假设和/或基于信号的某些线性结构的假设。 Lenart等人的最新研究。 （2008）[13]显示，有现代工具可用于分析循环平稳信号，它们基于对观测信号进行重采样的思想。本文的目的是展示一种称为二次采样的重采样技术在循环平稳信号的频率识别中的适用性。理论结果伴随着对车轮轴承产生的循环平稳信号进行频率分析的应用，其中一个没有任何损坏，而另外两个则具有两种不同类型的故障。结果表明，可以通过基于重采样的频率分析以及随后基于统计测试的频率识别来识别轴承的正常操作条件和异常操作条件。

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