Exponentially weighted moving average chart with a likelihood ratio test for monitoring autocorrelated processes
Exponentially weighted moving average chart with a likelihood ratio test for monitoring autocorrelated processes
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DOI：
10.1002/qre.2602
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发表时间：
2019-12
影响因子：
2.3
通讯作者：
Fu‐Kwun Wang;Xiao-Bin Cheng
中科院分区：
文献类型：
--
作者：
Fu‐Kwun Wang;Xiao-Bin Cheng
本文提出了一种基于似然比检验的指数加权移动平均图，用于自相关过程的均值和方差移动的同时监测。利用一种简单的方法将正自相关数据转化为负自相关数据。所提出的图表的平均运行长度来自模拟方法。我们提出的图表的性能进行了比较与一些现有的图表。结果表明，所提出的图表提供了更好的性能，同时检测过程中的均值和方差的大范围的变化。此外，还提供了一阶自回归模型下不同图表的经济表现。以供暖、通风和空调模块中的步进电机真实的例，说明了该方法的应用。
In this article, an exponential weighted moving average chart based on a likelihood ratio test is developed to monitor the mean and variance shifts simultaneously for autocorrelated processes. A simple method is used to transform the positively autocorrelated data to the negatively autocorrelated data. The average run length of the proposed chart is derived from a simulation approach. The performance of our proposed chart is compared with some existing charts. The results show that the proposed chart provides better performance for detecting a wide range of shifts in the process mean and variance simultaneously. Additionally, the economic performance of different charts under the first‐order autoregressive model is provided. A real example of a stepper motor in the heating, ventilation, and air conditioning module is used to demonstrate the application of the proposed method.