As one of the most important and essential elements of machines, rolling element bearings always fail due to the severe operating environment. Bearing failures usually result in the periodic impulses, which are the most crucial feature for the bearing diagnosis. These impulses may be overwhelmed by the background noises or other unrelated components. Many traditional features in the time domain such as kurtosis and root mean square (rms) are invalid in some cases. They are ineffective in detecting periodic impulses. This paper proposed a novel frequency-domain index, named envelope spectrum (ES) L-kurtosis (ESLK), to detect the periodicity of impulses. In this paper, simulations and bearing degradation data are utilized to clarify the properties of ESLK. In addition, this paper also proposed an ESLK-based method to locate the frequency range of periodic impulses. This method is verified by simulations and an experiment. The results illustrate that the proposed method performs better than the traditional kurtosis-based method.

中文翻译：

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包络谱L-峰态及其在滚动轴承故障检测中的应用

作为机器最重要和必不可少的元件之一，滚动轴承总是由于恶劣的运行环境而发生故障。轴承故障通常会导致周期性脉冲，这是轴承诊断最关键的特征。这些脉冲可能会被背景噪音或其他不相关的成分淹没。时域中的许多传统特征，例如峰度和均方根 (rms) 在某些情况下是无效的。它们在检测周期性脉冲方面无效。本文提出了一种新的频域指标，称为包络谱 (ES) L-峰态 (ESLK)，用于检测脉冲的周期性。在本文中，模拟和轴承退化数据被用来阐明 ESLK 的特性。此外，本文还提出了一种基于 ESLK 的方法来定位周期脉冲的频率范围。通过仿真和实验验证了该方法。结果表明，所提出的方法比传统的基于峰度的方法性能更好。