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A Sliding Singular Spectrum Entropy Method and Its Application to Gear Fault Diagnosis

机译:滑动奇异谱熵方法及其在齿轮故障诊断中的应用

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Entropy changes with the variation of the system status. It has been widely used as a standard for the determination of system status, quantity of system complexity and system classification. Based on the singular spectrum entropy of traditional calculation method, a sliding singular spectrum entropy method is proposed to use for singularity detection and extraction of impaction signal. Each original signal point is intercepted a neighborhood points of the signal with a given length and the singular spectrum entropy for the intercepted signal is calculated. A surrogate signal with the same length as the original signal is acquired by point-to-point calculation. Numerical simulation and gear fault diagnosis experiment are studied to verify the proposed method, the results show that the method is valid for the reflection on the changing of system status, singularity detection and the extraction of the weak fault feature signal mixed in the strong background signal.
机译:熵随系统状态的变化而变化。它已被广泛用作确定系统状态的标准,系统复杂性和系统分类的数量。基于传统计算方法的奇异谱熵,提出了一种滑动奇异谱熵方法,用于奇异性检测和瞬发信号的提取。每个原始信号点被拦截,计算具有给定长度的信号的邻域点,并且计算截获信号的奇异频谱熵。通过点对点计算获取与原始信号相同长度的代理信号。研究了数值模拟和齿轮故障诊断实验,验证了所提出的方法,结果表明该方法有效地对系统状态的变化,奇点检测和弱故障特征信号的反射有效,强制背景信号混合了弱故障特征信号。

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