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Fault diagnosis of natural gas compressor based on EEMD and Hilbert marginal spectrum

机译:基于EEMD和希尔伯特边际谱的天然气压缩机故障诊断。

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The paper utilizes ensemble empirical mode decomposition (EEMD) and Hilbert marginal spectrum for the fault diagnosis of the reciprocating compressor on the offshore platform of WZ12-1, aiming at the non-stationary and nonlinear characteristics of vibration signals collected from the faulty compressor. First, the EEMD algorithm self-adaptively anti-aliasing decomposes the vibration signal into a set of intrinsic mode function of different frequency bands. Then, the Hilbert marginal spectrum with some advantages in frequency resolution is used to extract the fault feature. Next, the proposed method succeeds in diagnosing the fault of the reciprocating compressor. The results show that the proposed method is feasible.
机译:针对WZ12-1海上平台往复式压缩机的故障,本文利用集成经验模态分解(EEMD)和希尔伯特边际谱对故障压缩机进行了故障诊断,针对的是从故障压缩机中收集到的振动信号的非平稳性和非线性特征。首先,EEMD算法自适应抗混叠将振动信号分解为一组不同频带的固有模式函数。然后,利用在频率分辨率上具有一定优势的希尔伯特边际频谱来提取故障特征。接下来,所提出的方法成功地诊断了往复式压缩机的故障。结果表明,该方法是可行的。

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