基于非负矩阵分解的单通道故障特征分离方法

针对单通道振动信号的多特征分离问题,提出了一种基于正交非负矩阵分解的故障特征提取方法。首先,采用短时傅里叶变换,利用时频分布来描述信号中的局部故障特征,通过核心一致性指标评估子空间维数;然后,在幅值谱矩阵分解的基础上,通过正交性约束实现低维嵌入分量信息的分离,获取局部特征的准确描述;最后,采用相位恢复理论重构出特征波形,对仿真信号和滚动轴承故障数据进行了测试。结果表明,所提出的方法能利用单通道信号有效地分离出微弱的局部故障特征,为机械状态的早期故障诊断识别提供了一种有效手段。

Separation of Fault Features from a Single Channel Mechanical Signal Using Non-Negative Matrix Factorization

A new feature extraction method for localized faults was proposed in order to distinguish fault features in a single channel vibration signal. This paper combines the concepts of time-frequency distribution with non-negative matrix factorization to propose a novel time-frequency matrix factorization method to extract representations of localized faults. The short-time Fourier transform was adopted to describe the localized faults of the vibration signal, and the subspace dimension was decided with a consistency index. Then, based on the decomposition of the time-frequency spectrum with orthogonal constraints, the low dimensional embedding was effectively separated. Finally, the theory of phase recovery was adopted to reconstruct waveforms of the localized fault features of interest. The simulation and rolling element bearing results showed that the proposed method was effective at extracting fault features of a single channel vibration signal and improved recognition accuracy in a mechanical system.