基于卷积稀疏表示及等距映射的轴承故障诊断

针对卷积稀疏表示(convolution sparse representation,简称CSR)在轴承故障脉冲提取过程中过于依赖惩罚因子的缺点,提出了一种基于卷积稀疏表示、希尔伯特变换(Hilbert transform,简称HT)以及流形学习降维相结合的轴承故障诊断方法。首先,通过在不同惩罚因子下的CSR提取不同稀疏特征的脉冲;其次,针对提取的一系列脉冲进行希尔伯特变换,构造脉冲包络空间;最后,利用等距映射(isometric feature mapping,简称Isomap)流形学习算法对脉冲包络空间求解低维本征包络,以实现故障诊断。通过仿真数据以及台架实验数据验证表明:基于CSRHT-Isomap算法的轮对轴承故障诊断方法可以很好地提取轴承内圈及滚动体故障特征,通过与基于聚合经验模态分解和小波包变换的包络空间算法进行比较,证明该方法在提取本征包络、强化本征包络谱以及放大故障特征频率的谐波数方面具备较大优势。

Fault Diagnosis of Bearing Based on CSR-ISOMAP

The performance of convolution sparse representation (CSR) in extracting impulses is sensitive to its improper penalty parameters. A novel fault detection method which is based on the combination of CSR, Hilbert transform (HT) and manifold learning is proposed. The impulses with different sparse characteristics are extracted by CSR at different penalty parameters. The impulse-envelope space is constructed with the Hilbert transform on the extracted impulses. The manifold based on Isomap is executed to learn the low-dimensionality intrinsic envelope of vibration signals for fault detection. The capability of the proposed fault detection method is verified by fault simulations as well as controlled experimental tests. The results show that the proposed fault detection method based on CSR-HT-Isomap can extract the fault characteristics of the inner and rolling fault. The method is also compared with two other envelope spaces spanned by the Hilbert transform on ensemble empirical mode decomposition and wavelet packet transform, respectively. The comparison shows that the CSR-Hilbert Transform-Isomap method is superior in extracting the intrinsic envelope, strengthening the amplitude of intrinsic envelope spectra and enlarging the harmonic number of fault-characteristic frequency.