基于振动特征的滚珠丝杠副预紧力丧失诊断研究

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振动与冲击 ›› 2018, Vol. 37 ›› Issue (12) : 201-206.

论文

王志荣，王禹林，陈超宇，周长光，欧屹，冯虎田

作者信息 +

A study on fault diagnosis of ball screw preload loss based on vibration signals

WANG Zhirong，WANG Yulin，CHEN Chaoyu， ZHOU Changguang，OU Yi，FENG Hutian

Author information +

文章历史 +

摘要

滚珠丝杠副预紧力丧失将导致机床进给精度及加工质量受到显著影响，实时监测滚珠丝杠副预紧力的变化并对其健康状态作出诊断具有重大意义。本文提出了一种将EMD（Empirical Mode Decomposition）与MSE（Multi-scale Entropy）相结合的信号处理方法进行预紧力丧失的故障诊断。设计了一种新型双螺母预紧力调节装置，用于采集不同预紧力水平下的螺母振动信号，通过EMD分解得到振动信号第一特征频率附近的IMF（Intrinsic Mode Function）分量，接着计算该IMF多个尺度下的熵值，最后分别以多尺度熵值与第一特征频率为特征向量建立BP神经网络进行预紧力丧失的诊断。诊断结果表明：以多尺度熵作为故障诊断特征向量时，神经网络的诊断正确率相比后者提高了50%。研究结果可用于基于振动特征的滚珠丝杠副预紧力丧失的故障诊断，对于促进滚珠丝杠副健康状态监测方法的发展具有重大意义。

Abstract

Preload loss in a ball screw will significantly deteriorate the feed precision; therefore, real time monitoring of preload variation of ball screw is necessary. In this paper, a signal processing method combing EMD with MSE was put forward to conduct the fault diagnosis of ball screw preload loss. A new preloadadjustable device was designed, based on which, the vibration signals at different preload levels were collected. The IMF near the first feature frequency of the vibration signal was obtained by EMD. Then multiscale entropy of the IMF was calculated. Finally a BP neural network was established to confirm that diagnosis using multiscale entropy as a feature vector was better than that using first feature frequency. The analysis results should be great significance to promote the development of ball screw health monitoring.

导出引用

王志荣，王禹林，陈超宇，周长光，欧屹，冯虎田. 基于振动特征的滚珠丝杠副预紧力丧失诊断研究[J]. 振动与冲击, 2018, 37(12): 201-206

WANG Zhirong，WANG Yulin，CHEN Chaoyu， ZHOU Changguang，OU Yi，FENG Hutian. A study on fault diagnosis of ball screw preload loss based on vibration signals[J]. Journal of Vibration and Shock, 2018, 37(12): 201-206

参考文献

[1] 王影. 滚珠丝杠传动系统的典型失效分析[J]. 精密制造与自动化, 2008(4):29-30.
WANG Ying. Typical failure analysis of ball screw drive system [J]. Precise Manufacturing & Automation, 2008(4): 29-30.
[2] 陈勇将, 汤文成, 王洁璐. 滚珠丝杠副刚度影响因素及试验研究[J]. 振动与冲击, 2013, 32(11):70-74.
CHEN Yong-jiang, TANG Wen-cheng, WANG Jie-lu. Influence factors on stiffness of a ball screw [J]. Journal of Vibration and Shock, 2013, 32(11): 70-74.
[3] 王金东, 代梅, 夏法锋,等. 基于EMD信息熵和支持向量机的往复压缩机轴承故障诊断[J]. 流体机械, 2014(7):43-46.
WANG Jin-dong, DAI Mei, XIA Fa-feng, et al. Fault diagnosis for reciprocating compressor bearings based on EMD-information entropy and SVM [J]. Fluid Machinery, 2014(7): 43-46.
[4] 徐可君, 秦海勤, 江龙平. 基于EMD和HHT的航空发动机转子-机匣振动信号分析[J]. 振动与冲击, 2011, 30(7):237-240.
XU Ke-jun, QIN Hai-qin, JIANG Long-ping. Rotor-case vibration signal analysis of an aero engine based on EMD and HHT [J]. Journal of Vibration and Shock, 2011, 30(7): 237-240.
[5] Costa M, Goldberger A L, Peng C K. Multiscale entropy analysis of biological signals [J]. Physical Review E Statistical Nonlinear & Soft Matter Physics, 2005, 71(2 Pt 1):021906-021906.
[6] 乔世权, 周万珍, 陈书旺. 膝关节红外热像图的多尺度熵算法分析研究[J]. 数学的实践与认识, 2016(16):141-145.
QIAO Shi-quan, ZHOU Wan-zheng, CHEN Shu-wang. Research on multiscale entropy algorithm for infrared image of knee joint [J]. Mathematics in Practice and Theory, 2016(16): 141-145.
[7] 郑近德, 程军圣, 杨宇. 基于多尺度熵的滚动轴承故障诊断方法[J]. 湖南大学学报(自科版), 2012, 39(5):38-41.
ZHENG Jin-de, CHENG Jun-sheng, YANG Yu. A rolling bearing fault diagnosis approach based on multiscale entropy. Journal of Hunan University (Natural Science) [J].2012, 39(5): 38-41.
[8] 肖瑛, 殷福亮. 解相关EMD:消除模态混叠的新方法[J]. 振动与冲击, 2015, 34(4):25-29.
XIAO Ying, YIN Fu-liang. Decorrelation EMD: a new method of eliminating mode mixing [J]. Journal of Vibration and Shock, 2015, 34(4): 25-29.
[9] 张龙, 黄文艺, 熊国良. 基于多尺度熵的滚动轴承故障程度评估[J]. 振动与冲击, 2014, 33(9):185-189.
ZHANG Long, HUANG Wen-yi, XIONG Guo-liang. Assessment of rolling element bearing fault severity using multi-scale entropy [J]. Journal of Vibration and Shock, 2014, 33(9): 185-189.
[10] 刘强, 周瑞忠, 刘宇航. 基于HHT变换的结构地震响应与能量计算分析[J]. 武汉大学学报(工学版), 2009, 42(6):780-784.
LIU Qiang, ZHOU Rui-zhong, LIU Yu-hang. Computation and analysis of seismic response and energy based on Hilbert-Huang transform [J]. Engineering Journal of Wuhan University, 2009, 42(6): 780-784.
[11] Richman J S, Moorman J R. Physiological time-series analysis using approximate entropy and sample entropy [J]. Ajp Heart & Circulatory Physiology, 2000, 278(6):H2039-H2049.
[12] Costa M, Goldberger A, Peng C. Multiscale entropy analysis of physiologic time series [J]. 2001.
[13] Feng G H, Pan Y L, Feng G H, et al. Investigation of ball screw preload variation based on dynamic modeling of a preload adjustable feed-drive system and spectrum analysis of ball-nuts sensed vibration signals[J]. International Journal of Machine Tools & Manufacture, 2012, 52(1):85-96.
[14] 常永寿, 冯虎田, 韩军,等. 滚珠丝杠副可靠性试验台测控系统设计[J]. 组合机床与自动化加工技术, 2016(2):47-50.
CHANG Yong-shou, FENG Hu-tian, HAN Jun, YANG Xue. Design of control system for the reliability of ball screw [J]. Modular Machine Tool & Automatic Manufacturing Technique, 2016(2): 47-50.
[15] Zhou C G, Feng H T, Chen Z T, et al. Correlation between preload and no-load drag torque of ball screws [J]. International Journal of Machine Tools & Manufacture, 2015, 102:35-40.
[16] 张亚. 滚珠丝杠副接触力学振动模型与特征提取方法研究[D]. 东南大学, 2013.
ZHANG Ya. Research on contact mechanics vibration model and feature extraction method of ball screw [D]. Southeast University, 2013.