文献类型：期刊文献

英文题名：Servo axis incipient degradation assessment of CNC machine tools using the built-in encoder

作者：Li, Yong[1];Zhao, Ming[2];Zhou, Shaoping[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Sch Mech & Power Engn, Key Lab Pressure Syst & Safety, Shanghai, Peoples R China;[2]Xi An Jiao Tong Univ, Sch Mech Engn, Xian, Shaanxi, Peoples R China

年份：2020

卷号：106

期号：9-10

起止页码：4293

外文期刊名：INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

收录：;EI(收录号：20200408072245);WOS:【SCI-EXPANDED(收录号:WOS:000507368100002)】；

基金：The work is supported by the National Natural Science Foundation of China (No. 51505147 and No. 51875434) and partly supported by the Fundamental Research Funds for the Central Universities (No. 222201514315), which is highly appreciated by the authors.

语种：英文

外文关键词：Incipient degradation assessment; Built-in encoder; CNC machine tools; FDW method; GI-guided denoising scheme

摘要：Servo axis system has been widely applied in the high-precision CNC machine tools. Its performance degradation may lead to the degradation of whole machine and ultimately result to the accuracy degradation of parts manufacturing. Thus, health assessment of the servo axis is very essential, especially for those in-service CNC machine tools. However, restricted by the complexity of servo axis structure, weak signal of incipient degradation, and limited sensors' installation space, traditional degradation evaluation methods, such as the vibration based scheme, are very difficult to be applied in real service environment directly. In this paper, a new methodology is established for servo axis incipient degradation assessment by reusing the position fluctuation information captured by built-in encoder. Firstly, to highlight the torsional behavior of the servo axis components, the instantaneous angular acceleration (IAA) is estimated by using the position fluctuation information with frequency domain weighting (FDW) method. After that, the wavelet packet transform (WPT) is employed for decomposition of these nonstationary IAA signals. Finally, a Gini index (GI)-guided denoising scheme is established for incipient degradation feature reconstruction. The effectiveness of the proposed method is investigated by simulations; thereafter, it is applied for the X-axis assessment of an in-service high-precision vertical machining center. All the results illustrate that the proposed method is sensitive to incipient degradation of the rotating components and offers an alternative solution for health assessment of servo axis.