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Chapter 77 Fault Diagnosis of Reciprocating Compressor Using Empirical Mode Decomposition-Based Teager Energy Spectrum of Airborne Acoustic Signal

机译:第77章往复式压缩机的故障诊断使用经验模型分解的空气传播信号信号

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Due to the presence of several rotating and reciprocating components, the acoustic signal obtained from the compressor normally involves transient impact and noise. The nonlinear and non-stationary response also contribute to the corruption of the useful information and lead to the diagnosing difficulty using the traditional methods. Due to the high nonstationary characteristic, time-frequency domain analysis can be a good choice for processing the airborne signal. In this paper, a new method based on empirical mode decomposition (EMD) and the Teager energy operator (TEO) is proposed to extract the fault features from the airborne acoustic signal for the reciprocating compressor fault diagnosis. The very unique advantage of TEO in detecting the transient response from a signals is exploited, and be applied to track the total mechanical energy that is responsible for generating the acoustic signal. Firstly, the signal is decomposed in the monocomponents by using EMD to satisfy the monocomponent criterion of the Teager energy operator. Next, the appropriate intrinsic mode function (IMF) is selected based on the correlation coefficient for obtaining the instantaneous energy by TEO. Finally, the spectrum analysis is done on the energy series for identifying the repeating frequency of the periodic impulses and thereby to diagnose the induced compressor faults under broad range of discharge pressures. The proposed method was also compared with the existing state of the art techniques like Hilbert energy spectrum and traditional spectral analysis. The comparison study shows the effectiveness of the proposed method over the existing methods in diagnosis of reciprocating compressor faults based on airborne acoustic signal analysis.
机译:由于存在多个旋转和往复组件,从压缩机获得的声学信号通常涉及瞬态冲击和噪声。非线性和非稳定性响应也有助于损坏有用信息,并通过传统方法导致诊断难度。由于高的非间断特征,时间频域分析可以是处理空气信号的良好选择。本文提出了一种基于经验模式分解(EMD)和TEAGET能量运算符(TEO)的新方法,以从空中声学信号中提取往复式压缩机故障诊断的故障特征。利用TEO在检测来自信号的瞬态响应时的非常独特的优点,并应用于跟踪负责产生声学信号的总机械能。首先,通过使用EMD来满足茶叶能量操作员的单一组分标准,信号在单一组分中分解。接下来,基于TEO获得瞬时能量的相关系数来选择适当的内在模式函数(IMF)。最后,在能量序列上进行频谱分析,用于识别周期性脉冲的重复频率,从而在广泛的放电压力下诊断诱导的压缩机故障。该方法也与现有的现有技术的现有技术进行了比较,如Hilbert能谱和传统光谱分析。比较研究表明了基于空机声学信号分析的诊断方法对现有方法的有效性。

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