Abstract

In functional sequential process monitoring, a process is characterized by sequences of observations called profiles which are monitored over time for stability. The goal is to halt a process when the process generating these observations deviates from a specified in control standard. We propose a Bayesian sequential process control (SPC) methodology which uses wavelets to monitor the functional responses and detect out of control profiles. Our contribution is to propose a solution to the growing computational cost by constructing an efficient and accurate approximation to the posterior distribution of the wavelet coefficients, without recourse to Markov chain Monte Carlo.

摘要

在功能顺序过程监控中，过程的特征在于称为配置文件的观察序列，这些观察序列随着时间的推移进行稳定性监控。目标是在生成这些观察结果的过程偏离控制标准中的指定时停止过程。我们提出了一种贝叶斯顺序过程控制 (SPC) 方法，该方法使用小波来监控功能响应并检测失控配置文件。我们的贡献是通过构建小波系数的后验分布的有效且准确的近似来提出解决不断增长的计算成本的方法，而无需求助于马尔可夫链蒙特卡罗。