期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
Sensorless Monitoring of a Motor-Drive Machanical System Based on Adaptive Signal Decomposition 被引量:1
1
作者 MENG Qing-feng JIAO Li-cheng 《International Journal of Plant Engineering and Management》 2006年第1期1-7,共7页
A method for estimating current harmonics of an induction motor is introduced which is used for sensorless monitoring of a mechanical system driven by the motor. The method is based on an adaptive signal representatio... A method for estimating current harmonics of an induction motor is introduced which is used for sensorless monitoring of a mechanical system driven by the motor. The method is based on an adaptive signal representation and is proposed to extract weak harmonics from a noisy current signal, especially in the presence of additive interference caused by transient modulation waves. As an application, a rotor unbalance experiment of rotating machinery driven by an induction motor is carried out, The result shows that the eccentricity harmonic magnitude of a current signal obtained by the method represents the rotor unbalance conditions sensitively. Vibration analysis is used to validate the proposed method. 展开更多
关键词 sensorless monitoring current harmonics adaptive signal representation rotor unbalance
下载PDF
Two-Dimensional Direction Finding via Sequential Sparse Representations
2
作者 Yougen Xu Ying Lu +1 位作者 Yulin Huang Zhiwen Liu 《Journal of Beijing Institute of Technology》 EI CAS 2018年第2期169-175,共7页
The problem of two-dimensional direction finding is approached by using a multi-layer Lshaped array. The proposed method is based on two sequential sparse representations,fulfilling respectively the estimation of elev... The problem of two-dimensional direction finding is approached by using a multi-layer Lshaped array. The proposed method is based on two sequential sparse representations,fulfilling respectively the estimation of elevation angles,and azimuth angles. For the estimation of elevation angles,the weighted sub-array smoothing technique for perfect data decorrelation is used to produce a covariance vector suitable for exact sparse representation,related only to the elevation angles. The estimates of elevation angles are then obtained by sparse restoration associated with this elevation angle dependent covariance vector. The estimates of elevation angles are further incorporated with weighted sub-array smoothing to yield a second covariance vector for precise sparse representation related to both elevation angles,and azimuth angles. The estimates of azimuth angles,automatically paired with the estimates of elevation angles,are finally obtained by sparse restoration associated with this latter elevation-azimuth angle related covariance vector. Simulation results are included to illustrate the performance of the proposed method. 展开更多
关键词 array signal processing adaptive array direction finding sparse representation
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部