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首页> 外文期刊>Mechanical systems and signal processing >Fault feature extraction of rotating machinery using a reweighted complete ensemble empirical mode decomposition with adaptive noise and demodulation analysis
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Fault feature extraction of rotating machinery using a reweighted complete ensemble empirical mode decomposition with adaptive noise and demodulation analysis

机译：利用自适应噪声和解调分析的加权完整整体经验模态分解提取旋转机械故障特征

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摘要

Fault feature extraction is crucial to detect failures as earlier as possible in fault diagnosis of rotating machinery. Due to the influence of environment noise and interference, the signal to noise ratio (SNR) of fault feature is relatively low in the measured signal. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is an improved method based on EEMD, which has been extensively applied to signal de-noising. The key problem for CEEMDAN is to determine the fault-related degree of a decomposed intrinsic mode function (IMF), especially in the presence of both Gaussian and non-Gaussian noises or interferences. However, most of the traditional assessment criterions are developed to describe the statistical parameters of IMFs, e.g. correlation coefficient and kurtosis, which ignore the specific characteristics of the fault and are easily affected by noise components. Therefore, a new criterion is proposed to quantify the fault-related degree of a vibration signal, in which the ratio of periodic modulation components caused by fault to the generalized interferences is defined. Then, a reweighted and reconstruction strategy of the decomposed IMFs is presented to obtain the de-noised signal based on the new criterion. Furthermore, in order to detect the fault-related modulation features in multi-frequency scales, a time-frequency representation (TFR) based demodulation analysis is employed, which guarantees an accurate extraction of the fault feature at the early stage of fault. The effectiveness of the proposed fault diagnosis method comparing to traditional methods are demonstrated by both numerical simulation and experimental studies. The results show that the proposed method achieves a better performance in terms of SNR improvement and fault feature detection, it can successfully detect the fault features in the presence of Gaussian and non-Gaussian noises.
机译：故障特征提取对于在旋转机械故障诊断中尽早发现故障至关重要。由于环境噪声和干扰的影响，故障信号的信噪比（SNR）在被测信号中相对较低。具有自适应噪声的完全集成经验模式分解（CEEMDAN）是基于EEMD的一种改进方法，已广泛应用于信号去噪。 CEEMDAN的关键问题是确定分解的固有模式函数（IMF）的故障相关程度，尤其是在同时存在高斯和非高斯噪声或干扰的情况下。但是，大多数传统的评估标准都是用来描述IMF的统计参数，例如相关系数和峰度，它们忽略了故障的特定特征，很容易受到噪声分量的影响。因此，提出了一种新的准则来量化振动信号的故障相关程度，其中定义了由故障引起的周期性调制分量与广义干扰的比率。然后，提出了一种新的分解IMF的加权和重建策略，以基于新准则获得降噪信号。此外，为了在多频率范围内检测与故障相关的调制特征，采用了基于时频表示（TFR）的解调分析，以确保在故障早期准确提取故障特征。数值模拟和实验研究都证明了所提出的故障诊断方法与传统方法相比的有效性。结果表明，该方法在信噪比提高和故障特征检测方面均具有较好的性能，能够在存在高斯噪声和非高斯噪声的情况下成功地检测出故障特征。

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