1、 滚珠丝杠副论文：基于性能退化模型的数控机床滚珠丝杠副寿命预测研究【中文摘要】作为数控机床关键传动部件的滚珠丝杠副,其寿命指标对数控机床加工性能的影响颇为重要。保证数控机床安全可靠的运行,尽量减少因滚珠丝杠副故障导致的停机而影响生产,以及预测和延长数控机床的维修周期等,一直是国内外机床行业及专家学者们关心和研究的重点。本文在国家重大科技专项“数控机床故障预警与诊断(项目编号：2009ZX04014-102)”和“国产高档数控机床在典型飞机结构件加工中的示范应用(2010ZX04015-011)”两个项目资助下,以某机械制造企业使用的长征 718 数控机床进给系统中,Y 方向滚珠丝杠副(THK
2、型)为研究对象,从数控机床结构特点及动力学角度,分析了影响滚珠丝杠副寿命的因素,得到了影响滚珠丝杠副寿命的关键因素是其工作负载和运行时间。研究得知滚珠丝杠副的性能退化是决定其寿命的关键,而性能退化主要体现为运行时的驱动力矩和振动的变化。因此,通过在机床滚珠丝杠副驱动电机上增设电流传感器和在滚珠丝杠副上增设振动传感器,经硬、软件系统进行信号采集、处理、运算、比较,监测滚珠丝杠副运行中的状态,预警滚珠丝杠副的故障,跟踪并预测滚珠丝杠副的寿命。采用经验模态分解方法对采集的传感器信号进行滤波处理后,提取信号时频域特征值,并采用主元素分析提取对滚珠丝杠副振动状态变化敏感的时频域特征值,以时间序列方式构成
3、特征值矩阵。通过切削试验测试并提取该机床常见的 20 种加工条件下的切削力信号与驱动电机电流信号,分析二者之间的映射关系,利用径向基函数网络构建切削力预测模型,在只有电流信号条件下对切削力进行预测,同时通过电流信号跟踪滚珠丝杠副的运行时间。考虑机床本身参数、加工条件及运行时间等因素,分析滚珠丝杠副在机床运行过程中负载情况,计算滚珠丝杠副的期望剩余寿命,以时间序列方式构成期望剩余寿命向量。采用动态模糊神经网络拟合振动状态特征量与期望剩余寿命之间的映射关系,建立性能退化模型,探求滚珠丝杠副性能退化规律,并以此预测滚珠丝杠副的剩余寿命。最后将该寿命预测方法编译成 Matlab 程序,并由数控机床故障
4、预警与性能评估系统主程序以一定时间间隔进行调用,实现对滚珠丝杠副剩余寿命现场在线预测及健康状态评估,为数控机床用户制定生产计划或维护维修计划提供依据。【英文摘要】The ball screw pair, as one of the key driving parts of the CNC machine, whose life is important to the CNC machine processing performance. It is always the focus of concern and research for the machine tool industry, e
5、xports and scholars at home and abroad, to ensure CNC machine operating safely and reliably, decrease the downtime caused by ball screw pair defaults as far as possible, predict and prolong the maintenance cycle of the CNC machine.Sponsored by the National Science and Technology Major Project of CNC
6、 Machine Faults Predict and Diagnosis (Project Number:2009ZX04014-102) and Domestic High-grade CNC Machine Demonstration Application in Typical Plane Structure Processing (2010ZX-04015-011), the Y direction ball screw pair (THK type) of the Changzheng 718 CNC machines feed driving systems is taken a
7、s reseach object in this thesis. The Changzheng 718 CNC machine is one of machine tools used in a mechanical manufacturing enterprise. The factors that influencing life of the ball screw pair, have been analyzed from the CNC machine structure characteristics and dynamics, and the conclusion is that
8、the key factors affecting life of ball screw pair are its working load and running time. It has discovered that the ball screw pairs performance degradation, which mainly reflected in the changes of driving torque and vibration while it runs, is the key to determine its life. Current sensors on the
9、drive motor of ball screw pair and vibration sensors on ball screw pair, therefore, are used to monitor the ball screw pairs state, warn its defaults, track and predict its life by means of the hardware and software systems for signals acquisition, processing, operation and compaeison.The signals co
10、llected are filtered by the Empirical Mode Decomposition algorithm, then the eigenvalues of the signals time and frequency domain are extracted out, and the Principle Component Analysis method is adopted to acquire the time-frequency domain edgenvalues which are sensitive to the vibarated state chan
11、ges of the ball screw pair. The eigenvalues form eigenvalue matrix in the time sequence at last.By cutting tests, the cutting force signals and drive motor current signals from the maichine tools 20 kinds of commonly processing conditions are measured and extracted, and the mapping relationship betw
12、een them is analysed. The Radial Basic Function network is used to construct the cutting force prediction model, which is used to prcdict cutting force only through current signals. At the same time, the current signals are used to track the ball screw pairs running time.After considering the parame
13、ters of the machine tool itself, processing conditions, running time, and some other factors, the load on ball screw pair is analyzed when machine tool is operating, and the expected residual life which consist of expected residual life vector in time series of the ball screw pair, is calculated.Dyn
14、amic Fuzzy Neural Network (DFNN) is used to fit the mapping relationship between vibrated state eigenvalues and expected residual life, and the performance degradation model, which is used to research the performance degradation law and predict the ball screw pair residual lifetime, is established t
15、hrough the DFNN.Finally, the life prediction method is complied into Matlab program, and called by the main program of CNC machine fault warning and performance evaluation system in a certain time intervals. Its realized to predict the residual life and assess the health condition live and online fo
16、r the ball screw pair. It can provide CNC machine users with evidence for production or maintenance plan.【关键词】滚珠丝杠副 寿命预测 性能退化 动态模糊神经网络【英文关键词】Ball Screw Pair Life Prediction Performance Degradation Dynamic Fuzzy Neural Network【目录】基于性能退化模型的数控机床滚珠丝杠副寿命预测研究 摘要 6-7 Abstract 7-8 第 1章 绪论 11-18 1.1 课题研究背景 1
17、1-12 1.2 滚珠丝杠副国内外研究现状 12-15 1.2.1 国外滚珠丝杠副的研究现状 13 1.2.2 国内滚珠丝杠副的研究现状 13-15 1.3 课题研究目的及意义 15-16 1.3.1 课题研究目的 15 1.3.2 课题研究意义 15-16 1.4 本文的研究内容 16-18 第 2 章 寿命分析与信息采集系统设计 18-31 2.1 机床滚珠丝杠副特点 18-20 2.1.1 滚珠丝杠副的结构特点 18-19 2.1.2 机床滚珠丝杠副的结构特点 19-20 2.2 机床滚珠丝杠副动力学分析 20-22 2.3 机床滚珠丝杠副寿命分析 22-27 2.3.1 滚珠丝杠副寿命
18、计算 22-23 2.3.2 平均负荷计算 23-24 2.3.3 滚珠丝杠副寿命影响因素 24-27 2.4 信息采集系统设计 27-31 2.4.1 传感器选型与安装 27-28 2.4.2 信号调理仪选择 28-29 2.4.3 采集卡与工控机选择 29 2.4.4 数据存储 29-31 第 3 章 振动信号处理及特征提取 31-48 3.1 振动信号滤波处理 31-37 3.1.1 零均值化处理 31 3.1.2 振动信号滤波处理 31-37 3.2 时频域分析及特征提取 37-46 3.2.1 时域分析及特征提取 37-43 3.2.2 频域分析及特征提取 43-45 3.2.3 时
19、频分析 45-46 3.3 振动信号特征优化 46-48 第 4章 切削力测试与电流信号分析 48-58 4.1 切削力测试方案设计 48-49 4.2 切削实验数据获取 49-50 4.3 切削力分析与预测 50-56 4.3.1 切削力信号与电流信号相关性分析 50-51 4.3.2 切削力预测模型 51-56 4.4 运行时间分析 56-58 第 5 章 性能退化模型建立 58-67 5.1 设备性能退化定义 58-59 5.2 动态模糊神经网络 59-64 5.2.1 动态模糊神经网络简介 59-60 5.2.2 动态模糊神经网络结构 60-62 5.2.3 动态模糊神经网络学习方法 62-64 5.3 退化模型建立 64-67 第 6 章 滚珠丝杠副寿命预测系统实现 67-78 6.1 滚珠丝杠副寿命预测系统软件设计 67-69 6.2 信息处理模块设计 69-73 6.3 寿命预测模块设计 73-78 结论 78-80 致谢 80-81 参考文献 81-86 攻读硕士学位期间发表的论文及参与的科研工作 86
