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

机译：更高阶时间频率分析作为健康监测的工具

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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：在机器状态监控中，许多与故障相关的振动信号非常非静止。时频表示包括传统上用于代表这种非静止信号的自然信号处理工具。该分析中使用的方法基于信号的二阶统计。本文讨论了在条件监测应用程序的上下文中使用高阶时间频率方法。本文概述了两类高阶表示：L-Wigner分布和切片的Wigner高阶分布。使用高阶分布引入了非振荡横向术的可能性，在传统的双线性时频方法中不遇到的问题。提出了减少这些交叉术语的技术。本文比较了使用乘用车的发动机块上测量的合成数据和振动信号的双线性和高阶时间频率方法的性能，以识别发动机阀门列车系统的动态特性。

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