一种叶片裂纹检测的稀疏共振解调算法

针对强相干噪声干扰下叶片振动信号中裂纹故障微弱特征的提取问题,提出了一种基于稀疏共振解调的诊断方法。首先,利用从中心集化多分辨分析处理机组上取得的原始振动信号进行子空间重构;其次, 对小波子空间信号进行希尔伯特包络解调,选取故障特征频率及其倍频成分能量占优的子空间;再次, 根据周期性故障稀疏模型,采用梳形滤波器分离故障特征频率及其倍频成分,构造故障分量参考信号;最后,结合故障参考信号对子空间重构信号进行小波降噪, 从而提取与叶片裂纹相关的微弱特征。在出现叶片裂纹故障的发电机组增压风机故障诊断案例分析中,仅采用多尺度分解无法在时域上得到周期性冲击故障特征。而采用所提出的基于稀疏共振解调方法进行信号处理后,强相干噪声得到了有效抑制, 从而突出了故障特征。

Sparsity-induced resonance demodulation method for blade crack detection

In order to extract incipient features caused by bladed machinery in the presence of coherent noises, a novel diagnostic approach using sparse demodulation operator is proposed. First, the recorded vibration signal from the bladed machinery is decomposed by the centralized multiresolution analyzing method and each subspace is reconstructed in the time domain. Second, the Hilbert demodulation method is performed on the reconstructed signals and some specific subspaces within which the harmonic tones of fault frequencies are dominant are selected. Third, comb filters are employed to separate the harmonic tones of fault frequencies such that a referential model for the fault features can be obtained. Finally, the reconstructed signals of the selected subspaces are denoised, via the wavelet threshold strategy combined with the referential model, to retrieve fault induced incipient features. The proposed method is applied to a fault diagnosis case study of a booster fan with blade cracks. It is found that the periodic impulsive features cannot be directly extracted in the time domain by merely using multiscale decomposition. However, with the proposed method, the actual fault features can be significantly enhanced after suppressing noises of strong coherence.