The wear and vibration of machine tool system are coupled and time-variant, which have significant influences on the dynamic performance of machine tool feed drive system in different time scales. In this study, the coupling effects between the wear of linear guide and the vibration of machine worktable system are studied based on the infinitesimal method. A nonlinear dynamic model is developed to analyze the wear and vibration failure mechanisms considering the uncertainty of parameters. To evaluate the dynamic reliability of the machine worktable system under multi-failure modes, a time-variant and conditional reliability approach based on the active learning Kriging model and Monte Carlo Simulation is proposed. The approach gets rid of repeated calculating of the real limit state function values and the calculation efficiency is enhanced greatly. Additionally, reliability-based sensitivity indices are presented to investigate the significance of random parameters to the reliability of the system. Besides of the reliability analysis for machine worktable systems, the proposed framework and corresponding method are also suitable for the reliability evaluation of other complex mechanical systems with the conditional multi-failure modes.

机床系统的磨损和振动是耦合的并且是时变的，这在不同的时间尺度上对机床进给驱动系统的动态性能有很大的影响。在这项研究中，基于无穷小方法研究了直线导轨的磨损与机床工作台系统的振动之间的耦合效应。考虑参数的不确定性，建立了非线性动力学模型来分析磨损和振动破坏机理。为了评估多故障模式下机床工作台系统的动态可靠性，提出了一种基于主动学习克里格模型和蒙特卡洛模拟的时变和条件可靠性方法。该方法省去了实际极限状态函数值的重复计算，大大提高了计算效率。此外，提出了基于可靠性的灵敏度指标，以研究随机参数对系统可靠性的重要性。除了对机器工作台系统的可靠性分析外，所提出的框架和相应的方法还适用于其他有条件多失效模式的复杂机械系统的可靠性评估。