基于特征空间降噪的局部保持投影算法及其在轴承故障分类中的应用

Locality Preserving Projections Based on Feature Space Denoising and Its Application in Bearing Fault Classificaiton

实际机械振动信号不可避免受到各种各样的噪声干扰,导致机械状态诊断结果误判等问题。目前的降噪算法主要都是针对时域振动信号,所需计算时间较长、占用存储空间较大。流形学习算法处理对象是样本特征空间数据,提出一种直接对样本特征空间进行奇异值分解降噪方法,再对降噪后的特征空间利用局部保留投影算法进行维数约简,通过1NN算法对设备运行状态进行识别。轴承故障仿真试验表明,与直接对时域信号进行降噪相比,所提方法能有效保证局部保持投影算法的降维效果,同时加快计算速度及减少所占存储空间。

The vibration signal measured in industrial applications is always contaminated by different noises, which leads to mistakes in fault diagnosis. Most of the de-noising algorithms deal with the time signal directly, which also are affected by computation time and memory space. The samples in feature space extracted from the original signal are more important than the time samples in fault diagnosis, which playing an important role in the application of manifold learning. A singular-value-based de-noising method is presented to denoise the feature samples, and then local preserving projection algorithm is used to reduce the feature dimension. Simulation and experiment results indicate that, comparing with de-noising the original time-domain signal, the proposed method can effectively speed up the computation process and decrease the memory space, while keeping the ability of dimension reduction and classification.