Kurtosis as a metric in the assessment of gear damage

V. Rao
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引用次数: 15

Abstract

Gear diagnostics is becoming an important area of research, especially in critical applications such as rotorcraft propulsion where safety is paramount. Many rotorcraft statistics illustrate the need for dedicated monitoring systems to reliably diagnose the faults. A gear system fails when it ceases to efficiently perform the function for which it was designed. This can be the result of a single catastrophic event or an accumulation of initially undetected and rather innocuous events in the gear system. It is well known that evaluation of statistical properties will give reasonable diagnostic indication of gear damage. Although there are a large number of such statistical attributes such as root mean square value, crest factor, skewness, kurtosis, and so on, kurtosis has emerged as a single number metric and one of the good indicators of damage of gears. Kurtosis can be estimated in both the time domain and the frequency domain. This paper discusses various variants of the kurtosis parameter such as FM4, NA4, NA4 * , NB4, and NB4 * , as well as their relative importance. The ability to use kurtosis beta distribution to detect toothwise gear faults is also discussed.
峰度作为评估齿轮损伤的指标
齿轮诊断正在成为一个重要的研究领域,特别是在关键应用,如旋翼飞机推进,安全是至关重要的。许多旋翼机的统计数据表明,需要专门的监测系统来可靠地诊断故障。齿轮系统失效时,它停止有效地执行其设计的功能。这可能是单一灾难性事件的结果,也可能是齿轮系统中最初未被发现且相当无害事件的累积。众所周知,统计性能的评估将给出齿轮损伤的合理诊断指示。虽然存在大量的统计属性,如均方根值、波峰因子、偏度、峰度等,但峰度已成为一个单一的数字度量,是齿轮损伤的良好指标之一。峰度可以在时域和频域估计。本文讨论了峰度参数FM4、NA4、NA4 *、NB4和NB4 *的各种变体及其相对重要性。本文还讨论了利用峰度分布检测齿形齿轮故障的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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