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首页> 外文期刊>Mechanical systems and signal processing >The Enkurgram: A characteristic frequency extraction method for fluid machinery based on multi-band demodulation strategy
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The Enkurgram: A characteristic frequency extraction method for fluid machinery based on multi-band demodulation strategy

机译：基于多频段解调策略的流体机械特征频率提取方法

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摘要

Modulation frequency extraction of fluid machinery is significant, not only for the condition monitoring and fault diagnosis of cooperative targets in industrial fields but also for object detection and information prejudgment of non-cooperative targets in military applications. However, most existing demodulation methods tend to show poor performance due to the ubiquitous heavy Gaussian and non-Gaussian noise. Especially, narrowband demodulation methods such as Kurtogram are in a dilemma due to the signal characteristic of multi-wideband carrier wave in fluid machinery. In this paper, the Enkurgram is proposed for multiple demodulation frequency bands selection based on the combination of energy factor and shape factor. This method shows excellent demodulation capability in both simulation analysis and applications to fluid machinery. Firstly, an Amplitude-Modulated (AM) signal model with multi-wideband carrier wave is established. Unlike other narrowband demodulation methods, multiple non-overlapping frequency bands are selected by Enkurgram for demodulation based on the composite criterion of Spectral Energy (SE) and Spectral Kurtosis (SK). Moreover, the demodulation performance is quantified by the proposed Signal Characterization Ratio (SCR). Secondly, the effectiveness of Enkurgram is validated by simulation signals under different noise interference situations, including white Gaussian noise, stochastic impulse interference, periodic impulse interference, and contaminated modulation wave. Finally, the proposed method is verified by the actual signals from centrifugal pump and propeller, respectively. The analysis results prove that the Enkurgram has satisfactory demodulation capability in dealing with signals under low Signal-to-Noise Ratio (SNR) levels or with non-Gaussian noise, which is the Achilles' heel of SK-based methods.
机译：调制频率提取流体机械是显着的，不仅是工业领域的协同目标的状态监测和故障诊断，而且还用于对象检测和信息偏见军事应用中的非合作目标。然而，由于普遍存在的重高斯和非高斯噪声，大多数现有的解调方法倾向于表现出差的性能。特别地，由于流体机械中的多宽带载波的信号特性，诸如KurtoGram等窄带解调方法是困境。在本文中，提出了基于能量因子和形状因子的组合的多个解调频带选择的eNKurgram。该方法在模拟分析和应用中显示出优异的解调能力，以流体机械。首先，建立具有多宽带载波的幅度调制（AM）信号模型。与其他窄带解调方法不同，基于光谱能量（SE）的复合标准和光谱峰值（SK）的复合标准，通过Enkurgram选择多个非重叠频带。此外，通过所提出的信号表征比（SCR）量化解调性能。其次，通过仿真信号在不同的噪声干扰情况下验证Enkurgram的有效性，包括白色高斯噪声，随机脉冲干扰，周期性脉冲干扰和污染的调制波。最后，所提出的方法分别由离心泵和螺旋桨的实际信号验证。分析结果证明，Enkurgram在处理低信噪比（SNR）水平或非高斯噪声下处理信号的令人满意的解调能力，这是基于SK的方法的Achilles的脚跟。

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