低概率故障过程的质量监控是现代质量管理的重要内容之一，作为统计过程控制(Statistical Process Control,SPC)的重要工具之一，事件发生间隔(Time Between Events,TBE)控制图常被用来监控低概率故障过程中的异常因素，但这些研究主要针对单变量过程，围绕TBE控制图的统计设计展开。实际中，影响产品质量的因素越来越多，通常需要对多个质量特性同时进行监控，且产品质量特性服从的分布模型参数往往是未知的。此外，根据过程失控状态下的历史样本数据，可以获得质量特性分布模型参数漂移的方向信息，此时采用相应的单边控制图监控过程的效率更高。针对这些问题，本项目以低概率故障过程为研究对象，考虑模型参数已知和未知的情形，分别从统计和经济视角构建性能指标来指导多变量过程监控方案的设计。本项目的研究成果将进一步完善质量控制的理论体系，为企业质量监控提供有效的理论和技术指导。

Low level of failure process monitoring is one of the important contents in modern quality management, as an important tool in statistical process control (SPC), time between events (TBE) control chart are widely used to monitor the assignable causes in the low level of failure processes. But those researches are mainly focused on the univariate processes. In practice, there are more and more factors affecting the quality of products, which are usually needed to be monitored together. Moreover, the parameters of the distribution model of the product quality characteristics are often unknown. In addition, according to the historical sample data in processes, the directional information of the parameter shift in the distribution of the quality characteristics can sometimes be obtained, under which circumstance, one-sided control charts are more efficient. So, considering the known and unknown process parameters, we construct the performance indexes from both statistical and economic points to guide the multivariate low level of failure process monitoring. The research results of this project will further improve the theory of the existing quality monitoring, and provide effective theoretical and technical guidance for enterprise quality control.