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Higher Order Time-Frequency Analysis as a Tool for Health Monitoring

机译：高阶时频分析作为健康监控工具

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摘要

In machine condition monitoring many fault-related vibration signals are highly non-stationary. Time-frequency representations comprise the natural signal-processing tool traditionally used for representing such non-stationary signals. The methods used in this analysis are based on the second order statistics of a signal. This paper discusses the use of higher order time frequency methods in the context of a condition monitoring application. The paper outlines the two classes of higher order representations: the L-Wigner distribution and the sliced Wigner higher order distribution. The use of higher order distributions introduces the possibility of non-oscillating cross-terms, a problem not encountered in conventional bilinear time-frequency methods. Techniques for reducing these cross-terms are presented. The paper compares the performance of bilinear and higher order time-frequency methods using synthetic data and vibration signals measured on the engine block of the passenger car for identification of the dynamic characteristics of the engine valve train system.
机译：在机器状态监测中，许多与故障相关的振动信号非常不稳定。时频表示包括传统上用于表示此类非平稳信号的自然信号处理工具。此分析中使用的方法基于信号的二阶统计量。本文讨论了在状态监视应用程序中使用高阶时频方法的问题。本文概述了两类高阶表示：L-Wigner分布和切片的Wigner高阶分布。高阶分布的使用引入了非振荡的交叉项的可能性，这是常规双线性时频方法中未遇到的问题。提出了减少这些交叉项的技术。本文利用合成数据和在乘用车发动机缸体上测得的振动信号比较了双线性和高阶时频方法的性能，以识别发动机气门机构系统的动态特性。

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