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Fault detection of mechanical drives under variable operating conditions based on wavelet packet Renyi entropy signatures

机译：基于小波包Renyi熵特征的可变工况机械驱动器故障检测

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摘要

In this paper we propose a novel approach for the diagnosis of gearboxes in presumably non-stationary and unknown operating conditions. The approach makes use of information indices based on the Renyi entropy derived from coefficients of the wavelet packet transform of measured vibration records. These indices quantify some statistical properties of instantaneous power of the generated vibration that are largely unaffected by changes in the operating conditions. The analysis is based on probability density of the envelope of a sum of sinusoidal signals with random amplitude and phase. Such an approach requires no a priori information about the operating conditions and no prior data describing physical characteristics of the monitored drive. The fault detection capabilities of the proposed feature set are demonstrated on a two-stage gearbox operating under different rotational speeds and loads with various seeded mechanical faults.
机译：在本文中，我们提出了一种新的方法来诊断齿轮箱，该齿轮箱可能是在非平稳和未知的工作条件下进行的。该方法利用基于从测量的振动记录的小波包变换的系数得出的Renyi熵的信息索引。这些指数量化了所产生的振动的瞬时功率的一些统计特性，这些统计特性在很大程度上不受操作条件变化的影响。该分析基于具有随机幅度和相位的正弦信号之和的包络的概率密度。这种方法不需要关于操作条件的先验信息，也不需要描述被监视驱动器的物理特性的先验数据。在具有不同种子机械故障的不同转速和负载的两级变速箱上演示了所提出功能集的故障检测能力。

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