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Fault Diagnosis of the Gyratory Crusher Based on Fast Entropy Multilevel Variational Mode Decomposition

机译:基于快速熵多级变分模式分解的旋转破碎机的故障诊断

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摘要

Gyratory crusher is a kind of commonly used mining machinery. Because of its heavy workload and complex working environment, it is prone to failure and low reliability. In order to solve this problem, this paper proposes a fault diagnosis method of the gyratory crusher based on fast entropy multistage VMD, which is used to quickly and accurately find the possible fault problems of the gyratory crusher. This method mainly extracts the vibration signal by combining fast entropy and variational mode decomposition, so as to analyze the components of the vibration signal. Among them, fast entropy is used to quickly determine the number of modes in the signal spectrum and the bandwidth occupied by the modes. The extracted parameters can be converted into the input parameters of VMD. VMD can accurately extract the modal components in the signal by inputting the number of modes and related parameters. Due to the differences between modes, using the same parameters to extract the modes often leads to inaccurate results. Therefore, the concept of multilevel VMD is proposed. The parameters of different modes are determined by fast entropy. The modes in the signals are separated and extracted with different parameters so that different signal modes can be accurately extracted. In order to verify the accuracy of the method, this paper uses the data collected from the rotary crusher to test, and the results show that the proposed FE method can quickly and effectively extract the fault components in the vibration signal.
机译:旋转式破碎机是一种常用的采矿机械。由于其繁重的工作量和复杂的工作环境,易于失败和低可靠性。为了解决这个问题,本文提出了一种基于快速熵多级VMD的响变碎机的故障诊断方法,用于快速准确地找到旋转破碎机的可能出现故障问题。该方法主要通过组合快速熵和变分模式分解来提取振动信号,从而分析振动信号的组件。其中,快速熵用于快速确定信号频谱中的模式数量和模式占用的带宽。可以将提取的参数转换为VMD的输入参数。 VMD可以通过输入模式数和相关参数来精确提取信号中的模态分量。由于模式之间的差异,使用相同的参数提取模式,通常会导致不准确的结果。因此,提出了多级VMD的概念。不同模式的参数通过快速熵确定。信号中的模式分离并用不同的参数提取,从而可以精确提取不同的信号模式。为了验证该方法的准确性,本文采用从旋转破碎机收集的数据进行测试,结果表明,所提出的FE方法可以快速且有效地提取振动信号中的故障分量。

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