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Alpha Stable Distribution Based Morphological Filter for Bearing and Gear Fault Diagnosis in Nuclear Power Plant

机译：基于核电厂轴承和齿轮故障诊断的基于α稳定的分布的形态学滤波器

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摘要

Gear and bearing play an important role as key components of rotating machinery power transmission systems in nuclear power plants. Their state conditions are very important for safety and normal operation of entire nuclear power plant. Vibration based condition monitoring is more complicated for the gear and bearing of planetary gearbox than those of fixed-axis gearbox. Many theoretical and engineering challenges in planetary gearbox fault diagnosis have not yet been resolved which are of great importance for nuclear power plants. A detailed vibration condition monitoring review of planetary gearbox used in nuclear power plants is conducted in this paper. A new fault diagnosis method of planetary gearbox gears is proposed. Bearing fault data, bearing simulation data, and gear fault data are used to test the new method. Signals preprocessed using dilation-erosion gradient filter and fast Fourier transform for fault information extraction. The length of structuring element (SE) of dilation-erosion gradient filter is optimized by alpha stable distribution. Method experimental verification confirmed that parameter alpha is superior compared to kurtosis since it can reflect the form of entire signal and it cannot be influenced by noise similar to impulse.
机译：齿轮和轴承在核电站旋转机械输电系统的关键部件中起重要作用。他们的国家条件对于整个核电站的安全和正常运行非常重要。基于振动的状态监测对于行星齿轮箱的齿轮和轴承比固定轴变速箱更复杂。行星齿轮箱故障诊断中的许多理论和工程挑战尚未解决，这对核电厂具有重要意义。本文进行了核电厂使用的行星齿轮箱的详细振动条件监测综述。提出了一种行星齿轮箱齿轮的新故障诊断方法。轴承故障数据，轴承仿真数据和齿轮故障数据用于测试新方法。使用扩张侵蚀梯度滤波器和用于故障信息提取的快速傅里叶变换的信号进行预处理。扩张侵蚀梯度滤波器的结构元素（SE）的长度通过α稳定分布进行了优化。方法实验验证证实，与峰氏症相比，参数α优异，因为它可以反映整个信号的形式，并且不能受到类似于脉冲的噪声的影响。

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