Wang Mengyang, Zhang Wenbao, Shao Mingzhen, Wang Guang
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Entropy (Basel). 2024 Jul 9;26(7):583. doi: 10.3390/e26070583.
To solve the separation of multi-source signals and detect their features from a single channel, a signal separation method using multi-constraint non-negative matrix factorization (NMF) is proposed. In view of the existing NMF algorithm not performing well in the underdetermined blind source separation, the β-divergence constraints and determinant constraints are introduced in the NMF algorithm, which can enhance local feature information and reduce redundant components by constraining the objective function. In addition, the Sine-bell window function is selected as the processing method for short-time Fourier transform (STFT), and it can preserve the overall feature distribution of the original signal. The original vibration signal is first transformed into time-frequency domain with the STFT, which describes the local characteristic of the signal from the time-frequency distribution. Then, the multi-constraint NMF is applied to reduce the dimensionality of the data and separate feature components in the low dimensional space. Meanwhile, the parameter WK is constructed to filter the reconstructed signal that recombined with the feature component in the time domain. Ultimately, the separated signals will be subjected to envelope spectrum analysis to detect fault features. The simulated and experimental results indicate the effectiveness of the proposed approach, which can realize the separation of multi-source signals and their fault diagnosis of bearings. In addition, it is also confirmed that the proposed method, juxtaposed with the NMF algorithm of the traditional objective function, is more applicable for compound fault diagnosis of the rotating machinery.
为了解决多源信号的分离问题并从单通道中检测其特征，提出了一种使用多约束非负矩阵分解（NMF）的信号分离方法。鉴于现有的NMF算法在欠定盲源分离中表现不佳，在NMF算法中引入了β散度约束和行列式约束，通过约束目标函数可以增强局部特征信息并减少冗余分量。此外，选择正弦钟窗函数作为短时傅里叶变换（STFT）的处理方法，它可以保留原始信号的整体特征分布。首先将原始振动信号通过STFT变换到时间-频率域，从时频分布描述信号的局部特征。然后，应用多约束NMF降低数据维度并在低维空间中分离特征分量。同时，构建参数WK对在时域中与特征分量重组的重构信号进行滤波。最终，对分离后的信号进行包络谱分析以检测故障特征。仿真和实验结果表明了所提方法的有效性，该方法可以实现多源信号的分离及其对轴承的故障诊断。此外，还证实了所提方法与传统目标函数的NMF算法相比，更适用于旋转机械的复合故障诊断。
