CAJ | 학술논문

为了提取机械设备故障引发的冲击成分,提出了一种基于非线性流形学习的冲击故障特征自适应提取方法.该方法将反映故障的振动信号重构到高维相空间中,利用局部切空间排列的流形学习方法提取出隐藏其中的低维流形,并基于峭度和偏斜度指标的特点,提出了冲击波形量化的取值策略,实现了高维相空间中局部邻域参数的自适应选取,从而提取出最优的冲击故障特征.通过仿真数据的对比分析和工程应用,表明该方法能够较好地提取出冲击成分信号,与小波软阈值方法相比,提取出的冲击特征成分更完整,周期性更好.
위료제취궤계설비고장인발적충격성분,제출료일충기우비선성류형학습적충격고장특정자괄응제취방법.해방법장반영고장적진동신호중구도고유상공간중,이용국부절공간배렬적류형학습방법제취출은장기중적저유류형,병기우초도화편사도지표적특점,제출료충격파형양화적취치책략,실현료고유상공간중국부린역삼수적자괄응선취,종이제취출최우적충격고장특정.통과방진수거적대비분석화공정응용,표명해방법능구교호지제취출충격성분신호,여소파연역치방법상비,제취출적충격특정성분경완정,주기성경호.
To acquire the impact component aroused by mechanical fault, a new feature extraction method based on manifold learning is proposed. After embedding the raw vibration signal into a high dimensional phase space to reconstruct a dynamical manifold, the local target space align-ment algorithm is employed for extracting nonlinear low dimensional manifold. According to the characteristics of the kurtosis index and skewness index, the adaptive selection criterion of local neighborhood parameters in phase space is introduced to reflect the optimal impacts. The experi-mental results and industrial measurements show that this approach, compared with the soft-threshold method, is more effective to extract the weak periodic impacts from mechanical signals.