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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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。

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	作者相关文章
	王志荣
	王禹林
	陈超宇
	周长光
	欧屹
	冯虎田

关键词 ：预紧力, 滚珠丝杠副, 振动信号, 故障诊断, 特征向量

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.

Key words： preload ball screw vibration signal fault diagnosis feature vector

收稿日期: 2016-12-27 出版日期: 2018-06-15

引用本文:

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. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(12): 201-206.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2018/V37/I12/201

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