Home > Published Issues > 2020 > Volume 9, No. 4, April 2020 >

Data Acquisition and Management Strategy for Motorized Spindle Test

Weizheng Chen , Zhicheng Zhang, Binbin Xu, and Fei Chen
School of Mechanical and Aerospace Engineering, Jilin University, Changchun, P.R.China
Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, Changchun, P.R.China

Abstract—Motorized spindle is the critical component in the CNC machine tool, and it is of considerable significance to obtain its overall performance and expose its weak link via conducting reliability test. Multi-dimensional signals with high sampling frequency and long storage period pose a great challenge to data collection and management process. This paper is devoted to proposing an efficient and systematic method for data acquisition, feature extraction, and data management methods. The redundancy of the original signal is removed by similarity analysis of feature matrixes, after which the most typical sample during the test is preserved as a data sample for data mining and data analysis. The proposed strategy can not only be applied in the motorized spindle but also be a guide for the test of similar mechanical system. 

Index Terms—condition monitoring, motorized spindle, data mining, data analysis

Cite: Weizheng Chen, Zhicheng Zhang, Binbin Xu, and Fei Chen, "Data Acquisition and Management Strategy for Motorized Spindle Test" International Journal of Mechanical Engineering and Robotics Research, Vol. 9, No. 4, pp. 618-623, April 2020. DOI: 10.18178/ijmerr.9.4.618-623

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.