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A novel scheme for fault detection of reciprocating compressor valves based on basis pursuit, wave matching and support vector machine

机译:基于基本追踪,波匹配和支持向量机的往复式压缩机气门故障检测新方案

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摘要

A scheme for fault detection of compressor valves based on basis pursuit (BP), wave matching and support vector machine (SVM) is presented. BP is applied to extract the main vibration component in the signal and suppress background noise. Wave matching is a new feature extraction method proposed in this paper. Instead of extracting features through commonly used indicators such as statistic measures or information entropy, wave matching extracts features by matching the vibration signal with parameterized waveform optimized by differential evolution (DE) algorithm. It only produces a small number of features and the features have clear physical meaning. SVM is employed in the fault classification because of its superiority in dealing with small sample problems. The results of real compressor valve signal analysis confirm that the proposed scheme can differentiate compressor valve faults with high accuracy and reliability.
机译:提出了一种基于基本追踪(BP),波动匹配和支持向量机(SVM)的压缩机气门故障检测方案。 BP用于提取信号中的主要振动分量并抑制背景噪声。波匹配是本文提出的一种新的特征提取方法。波匹配不是通过统计指标或信息熵等常用指标提取特征,而是通过将振动信号与通过差分演化(DE)算法优化的参数化波形进行匹配来提取特征。它仅产生少量特征,并且这些特征具有明确的物理含义。由于支持向量机在处理小样本问题方面的优势,因此被用于故障分类。实际的压缩机气门信号分析结果表明,该方案能够准确,可靠地判别压缩机气门故障。

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