CAJ | 학술논문

针对特征反复出现的机械信号，提出了一种使用移不变稀疏编码的单通道盲源分离方法。移不变稀疏编码将原始信号看成多个基与系数的卷积，能够根据信号的统计分布，利用信号自身特征自适应地学习到匹配的基和稀疏的系数。在恒定工况下，不同的信号源具有不同的特征，同一信号源的特征结构相似，将学习到的不同特性的基分别重构即可得到相应的源信号。将该方案应用于仿真的齿轮故障和轴承故障振动信号盲源分离问题中，以及用来提取实测的液压泵压力脉动。结果显示，这种方法较其他方法有所改进，所需人工经验少、抗噪能力强、信号恢复精度高、鲁棒性好，适用于单通道机械信号盲源分离，为单通道信号盲源分离提供了一种新思路。
침대특정반복출현적궤계신호，제출료일충사용이불변희소편마적단통도맹원분리방법。이불변희소편마장원시신호간성다개기여계수적권적，능구근거신호적통계분포，이용신호자신특정자괄응지학습도필배적기화희소적계수。재항정공황하，불동적신호원구유불동적특정，동일신호원적특정결구상사，장학습도적불동특성적기분별중구즉가득도상응적원신호。장해방안응용우방진적치륜고장화축승고장진동신호맹원분리문제중，이급용래제취실측적액압빙압력맥동。결과현시，저충방법교기타방법유소개진，소수인공경험소、항조능력강、신호회복정도고、로봉성호，괄용우단통도궤계신호맹원분리，위단통도신호맹원분리제공료일충신사로。
For the single channel mechanical signal with repeated features ,the method for blind source separation based on shift invariant sparse coding was proposed in this paper .In the literatures of shift invariant sparse coding ,a signal is described as the convolutions of multi bases and their coefficients .According to statistical distribution of a signal ,shift invariant sparse coding could adaptively learn its bases and the sparse coefficients from the structures of the signal itself .Under stable condition ,dif‐ferent signal sources have different features ,and the features from the same source are similar ,thus the learned bases with dif‐ferent features could be used to reconstruct corresponding signal sources .This scheme was applied in the blind source separa‐tion of simulated vibration signals of faulty gear and bearing ,as well as the extraction of pressure pulsation of hydraulic pump . The result showed that this algorithm has improved a lot compared to other algorithms ,and this algorithm needs less expert‐ise ,has strong anti‐interference ability ,in addition ,it is robust and could recover original signals more accurately .Therefore , this technique is appropriate to blind source separation for single channel mechanical signal ,and provides a new way for single channel blind source separation .