Wang Huaqing, Wang Mengyang, Li Junlin, Song Liuyang, Hao Yansong
College of Mechanical & Electrical Engineering, Beijing University of Chemical Technology, Chao Yang District, Beijing 100029, China.
Beijing Key Laboratory of High-end Mechanical Equipment Health Monitoring and Self-Recovery, Beijing University of Chemical Technology, Beijing 100029, China.
Entropy (Basel). 2019 Apr 28;21(5):445. doi: 10.3390/e21050445.
In order to separate and extract compound fault features of a vibration signal from a single channel, a novel signal separation method is proposed based on improved sparse non-negative matrix factorization (SNMF). In view of the traditional SNMF failure to perform well in the underdetermined blind source separation, a constraint reference vector is introduced in the SNMF algorithm, which can be generated by the pulse method. The square wave sequences are constructed as the constraint reference vector. The output separated signal is constrained by the vector, and the vector will update according to the feedback of the separated signal. The redundancy of the mixture signal will be reduced during the constantly updating of the vector. The time-frequency distribution is firstly applied to capture the local fault features of the vibration signal. Then the high dimension feature matrix of time-frequency distribution is factorized to select local fault features with the improved SNMF method. Meanwhile, the compound fault features can be separated and extracted automatically by using the sparse property of the improved SNMF method. Finally, envelope analysis is used to identify the feature of the output separated signal and realize compound faults diagnosis. The simulation and test results show that the proposed method can effectively solve the separation of compound faults for rotating machinery, which can reduce the dimension and improve the efficiency of algorithm. It is also confirmed that the feature extraction and separation capability of proposed method is superior to the traditional SNMF algorithm.
为了从单通道振动信号中分离并提取复合故障特征，提出了一种基于改进稀疏非负矩阵分解（SNMF）的新型信号分离方法。鉴于传统SNMF在欠定盲源分离中表现不佳，在SNMF算法中引入了一种约束参考向量，该向量可通过脉冲法生成。将方波序列构造为约束参考向量。输出的分离信号受该向量约束，且该向量会根据分离信号的反馈进行更新。在向量不断更新的过程中，混合信号的冗余度会降低。首先应用时频分布来捕捉振动信号的局部故障特征。然后对时频分布的高维特征矩阵进行分解，采用改进的SNMF方法选择局部故障特征。同时，利用改进的SNMF方法的稀疏特性可自动分离分离并提取复合故障特征。最后，通过包络分析来识别输出分离信号的特征并实现复合故障诊断。仿真和测试结果表明，该方法能有效解决旋转机械复合故障的分离问题，可降低算法维度并提高效率。同时也证实了该方法的特征提取和分离能力优于传统SNMF算法。
