数控机床最小维修的全寿命周期可靠性评估

摘要/Abstract

摘要：

摘要：基于叠加的对数线性过程(SLLP)，提出了多台数控机床在最小维修条件下的全寿命周期可靠性评估方法，建立了包含早期故障期和耗损故障期的浴盆曲线模型，定量分析了早期故障期的持续时间，给出了模型参数和可靠性指标的极大似然点估计和区间估计；同时，以多台数控机床现场故障截尾数据为例对模型进行验证.AIC值和拟合优度计算结果表明，SLLP模型优于叠加幂律过程模型，适用于数控机床的全寿命周期可靠性评估.

关键词: 数控机床, 最小维修, 极大似然估计, 全寿命周期, 可靠性评估

Abstract:

Abstract： Based on superposed loglinear process (SLLP), a whole lifecycle reliability assessment method for multiple numericallycontrolled (NC) machine tools with minimal repair was proposed. A bathtub curve model, which includes early failure period and wear failure period, was presented. The lasting time of early failure period was quantitatively analyzed, and the point maximum likelihood and interval estimators of model parameters and reliability indices were given. An example of real data with failure truncation from multiNC machine tools was taken to prove the proposed method. The results of AIC and goodnessoffit show that the SLLP model is better than that of the superposed powerlaw process model, which is suitable for whole lifecycle reliability assessment of NC machine tools.

Key words: numericallycontrolled machine tools, minimal repair, maximum likelihood estimation, whole lifecycle, reliability assessment

中图分类号:

任丽娜，芮执元，刘军，雷春丽. 数控机床最小维修的全寿命周期可靠性评估[J]. 上海交通大学学报（自然版）, 2015, 49(01): 19-23.

REN Lina,RUI Zhiyuan,LIU Jun,LEI Chunli. Whole Lifecycle Reliability Assessment of Multiple NC Machine Tools with Minimal Repair[J]. Journal of Shanghai Jiaotong University, 2015, 49(01): 19-23.

参考文献

［1］王智明, 杨建国, 王国强, 等. 多台数控机床最小维修的可靠性评估［J］. 哈尔滨工业大学学报, 2011, 43(7): 127130.

WANG Zhiming, YANG Jianguo, WANG Guoqiang, et al. Reliability assessment of multiple NC machine tools with minimal repair ［J］. Journal of Harbin Institute of Technology, 2011, 43(7): 127130.

［2］Wang Z M, Yu X. Loglinear process modeling for repairable systems with time trends and its applications in reliability assessment of numerically controlled machine tools ［J］. Proceedings of the Institution of Mechanical Reliability Engineers. Part O: Journal of Risk and Reliability, 2013, 227(1): 5565.

［3］王智明, 杨建国. 数控机床可靠性评估中的边界强度过程［J］. 上海交通大学学报, 2012, 46(10): 16221626.

WANG Zhiming, YANG Jianguo. Bounded intensity process and its applications in reliability assessment of NC machine tools ［J］. Journal of Shanghai Jiaotong University, 2012, 46(10): 16221626.

［4］Pulcini G. Modeling the failure data of a repairable equipment with bathtub type failure intensity ［J］. Reliability Engineering and System Safety, 2001, 71: 209218.

［5］许彬彬, 杨兆军, 陈菲, 等. 非齐次泊松过程的数控机床可靠性建模［J］. 吉林大学学报:工学版, 2011, 41(S2): 210214.

XU Binbin, YANG Zhaojun, CHEN Fei, et al. Reliability model of CNC machine tools based on nonhomogeneous Poisson process ［J］. Journal of Jilin University: Engineering and Technology Edition, 2011, 41(S2): 210214.

［6］廖小波. 机床故障率浴盆曲线定量化建模及应用研究［D］. 重庆: 重庆大学机械工程学院, 2010.

［7］魏领会. 数控机床可用性耦合建模及影响度分析［D］. 吉林: 吉林大学机械科学与工程学院, 2011.

［8］Mun B M, Bae S J. Optimal maintenance policy of repairable system with bathtub shaped intensity ［C］//International Conference on ICQR2MSE. Xi’an: IEEE, 2011: 431435.

［9］Park S, Balarishnan N, Zheng G. Fisher information in hybrid censored data ［J］. Statistics and Probability Letters, 2008, 78(16): 27812786.

［10］Guo H R, Liao H T, Zhao W B, et al. A new stochastic model for systems under general repairs ［J］. IEEE Transactions on Reliability, 2007, 56(1): 4049.

［11］Louit D M, Pascual R, Jardine A K S. A practical procedure for the selection of timetofailure models based on the assessment of trends in maintenance data ［J］. Reliability Engineering and System Safety, 2009, 94(10): 16181628.

［12］Chen X M. Using Akaike information criterion for selecting the field distribution in a reverberation chamber ［J］. IEEE Transactions on Electromagnetic Compatibility, 2013, 55(4): 664670.

［13］任丽娜, 芮执元, 李建华, 等. 修正的边界强度过程模型及其应用［J］. 计算机集成制造系统, 2014, 20(3): 595602.

REN Lina, RUI Zhiyuan, LI Jianhua, et al. Modified bounded intensity process model and its application ［J］. Computer Integrated Manufacturing Systems, 2014, 20(3): 595602.

[1]	管清宇, 夏品奇, 郑晓玲, 吴光辉. 复合材料层压板冲击后压缩强度拟合模型[J]. 上海交通大学学报, 2021, 55(11): 1459-1466.
[2]	赵亮，雷默涵，朱星星，王帅，凌正，杨军，梅雪松. 高精度数控机床主轴系统热误差的控制方法[J]. 上海交通大学学报, 2020, 54(11): 1165-1171.
[3]	张立波1，程浩忠1，曾平良2，张沈习1，周勤勇2. 基于Johnson分布体系的发输电系统可靠性评估[J]. 上海交通大学学报（自然版）, 2017, 51(5): 555-.
[4]	胡新明,王德禹. 基于迭代均值组合近似模型和序贯优化与可靠性评估法的船舶结构优化设计[J]. 上海交通大学学报（自然版）, 2017, 51(2): 150-.
[5]	拓占宇，黄奕乔，沈牧文，杨建国. 基于3次样条插值的数控机床几何与热复合定位误差建模[J]. 上海交通大学学报（自然版）, 2016, 50(05): 668-672.
[6]	冯文龙，黄奕乔，拓占宇，李慧敏，杨建国. 基于温度积分方法的大型数控机床光栅定位热误差建模及实时补偿[J]. 上海交通大学学报（自然版）, 2016, 50(05): 710-715.
[7]	姚晓栋1,3,黄奕乔1,马晓波2,薛波3,杨建国1. 基于时间序列算法的数控机床热误差建模及其实时补偿[J]. 上海交通大学学报（自然版）, 2016, 50(05): 673-679.
[8]	方鹏亚，常新龙，胡宽，张晓军. 基于区间不确定性的多学科可靠性设计[J]. 上海交通大学学报（自然版）, 2014, 48(10): 1451-1456.
[9]	王维，杨建国. 基于插值算法的数控机床复合误差补偿技术[J]. 上海交通大学学报（自然版）, 2014, 48(1): 12-21.
[10]	肖慧孝，杨建国，张毅. 基于状态空间模型的机床热误差动态建模[J]. 上海交通大学学报（自然版）, 2014, 48(1): 22-26.
[11]	朱小龙，杨建国，代贵松. 基于AVQ聚类和OIF-Elman神经网络的机床热误差建模[J]. 上海交通大学学报（自然版）, 2014, 48(1): 22-26.
[12]	王智明1,杨建国2. 数控机床不完全维修的贝叶斯可靠性评估[J]. 上海交通大学学报（自然版）, 2014, 48(05): 614-617.
[13]	杨漪1，姚晓栋1，杨建国1，张余升2，袁峰2. 基于主成分分析与BP神经网络相结合的机床主轴热漂移误差建模[J]. 上海交通大学学报（自然版）, 2013, 47(05): 750-753.
[14]	姜辉，杨建国，李自汉，姚晓栋. 基于误差分解的数控机床热误差叠加预测模型及实时补偿应用[J]. 上海交通大学学报（自然版）, 2013, 47(05): 744-749.
[15]	赵飞a，梅雪松a,b，姜歌东a,b，陶涛a,b，史建强a. 数控机床进给系统装配误差建模及特征分析[J]. 上海交通大学学报（自然版）, 2013, 47(05): 703-708.