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首页> 外文期刊>Mechanical systems and signal processing >A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy
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A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy

机译：基于自适应多尺度形态学滤波器和改进的层次排列熵的行星齿轮箱故障诊断方案

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Highlights•MHPE is proposed to extract the fault features.•MHPE can overcome the problems of MPE.•A fault diagnosis strategy based on AMMF and MHPE is developed.•The proposed method is validated using simulated and experimental signals.AbstractThe fault diagnosis of planetary gearboxes is crucial to reduce the maintenance costs and economic losses. This paper proposes a novel fault diagnosis method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes. In this method, AMMF is firstly adopted to remove the fault-unrelated components and enhance the fault characteristics. Second, MHPE is utilized to extract the fault features from the denoised vibration signals. Third, Laplacian score (LS) approach is employed to refine the fault features. In the end, the obtained features are fed into the binary tree support vector machine (BT-SVM) to accomplish the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault categories of planetary gearboxes.
机译： 突出显示 • 建议使用MHPE提取故障特征。 • MHPE可以克服MPE问题。 < / ce：list-item> • 故障诊断策略 • 使用模拟和实验信号验证了所提出的方法。 摘要 行星齿轮箱的故障诊断对于降低维护成本和经济损失至关重要。提出了一种基于自适应多尺度形态学滤波器（AMMF）和改进的层次排列熵（MHPE）的故障诊断方法，以识别行星齿轮箱的不同健康状况。在这种方法中，首先采用AMMF去除与故障无关的组件并增强故障特性。其次，MHPE用于从降噪的振动信号中提取故障特征。第三，采用拉普拉斯分数（LS）方法来完善断层特征。最后，将获得的特征馈入二叉树支持向量机（BT-SVM）中以完成故障模式识别。数值模拟和实验证明了该方法能够识别行星齿轮箱的不同故障类别。

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