An enhanced feature extraction model using lifting-based wavelet packet transform scheme and sampling-importance-resampling analysis |
| |
Authors: | Yixiang Huang Chengliang Liu Xuan F. Zha Yanming Li |
| |
Affiliation: | aSchool of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China;bExtension Systems International, National Institute of Standards and Technology, USA |
| |
Abstract: | The efficiency of data processing is critical for the on-line monitoring applications of industrial components and systems, both from the viewpoints of the rapid adaptation to the non-stationary signals and the cost of information storage and transmission. In this paper, we propose an enhanced feature extraction model for machinery performance assessment, which is based on the lifting-based wavelet packet transform (WPT) and sampling-importance-resampling methods. The lifting-based WPT decomposes the signals. Then the sampling-importance-resampling procedure is applied in the wavelet domain to extract the distribution information and compose the feature vectors. Finally, a support vector machine is used to assess the normal or abnormal condition based on these extracted features. To validate the proposed new model, an endurance test of pressure regulators has been carried out. Compared to the traditional wavelet packet method, the new model can not only keep the precision level but also improve the efficiency by over 60%. |
| |
Keywords: | Feature extraction Condition monitoring Sampling-importance-resampling algorithm Wavelet packet Lifting-based scheme Support vector machine |
本文献已被 ScienceDirect 等数据库收录! |
|