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Fault Diagnosis Method Based on Modified Multiscale Entropy and Global Distance Evaluation for the Valve Fault of a Reciprocating Compressor

机译:基于改进的多尺度熵的故障诊断方法和往复式压缩机阀故障的全局距离评估

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摘要

According to the nonlinearity, non-stationarity and multi-component coupling characteristics of reciprocating compressor vibration signals, a fault diagnosis method of a reciprocating compressor valve based on modified multiscale entropy (MMSE) and global distance evaluation (GDE) is proposed. First, the variational mode decomposition (VMD) method with superior anti-interference performance was utilized to analyse the strong non-stationarity vibration signals for all fault states. The modified multiscale entropy (MMSE) method provided for moving-average procedures by replacing mean-average coarse-grained procedures was developed for the vibration signals after de-noising and then the GDE method of overall parameter selection was introduced to evaluate the extracted MMSE and to select the optimal sensitivity scale feature. Finally, a binary tree of support vector machine (BTSVM) was selected as the classifier to identify the reciprocating compressor valve fault type. By analysing the experimental data, it can be shown that the method can effectively identify the fault type of the reciprocating compressor valve.
机译:根据往复式压缩机振动信号的非线性,非实用性和多组分耦合特性,提出了基于改进的多尺度熵(MMSE)和全局距离评估(GDE)的往复式压缩机阀的故障诊断方法。首先,利用具有卓越抗干扰性能的变分模式分解(VMD)方法来分析所有故障状态的强不良振动信号。通过替换平均平均粗粒程序提供用于移动平均程序的改进的多尺度熵(MMSE)方法,以便在去噪后的振动信号,然后引入了整体参数选择的GDE方法来评估提取的MMSE和选择最佳灵敏度尺度特征。最后,选择了支持向量机(BTSVM)的二叉树作为分类器以识别往复式压缩机阀故障类型。通过分析实验数据,可以示出该方法可以有效地识别往复式压缩机阀的故障类型。

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