Monitoring a process over time is so important in manufacturing processes to reduce the waste of money and time. Some charts as Shewhart, CUSUM, and EWMA are common to monitor a process with a single intended attribute which is used in different kinds of processes with various ranges of shifts. In some cases, the process quality is characterized by different types of profiles. The purpose of this article is to monitor profile coefficients instead of a process mean. In this paper, two methods are proposed for monitoring the intercept and slope of the simple linear profile, simultaneously. In this regard, two methods are compared here. The first one is the linear regression, and the one is the maximum entropy principle. The T2 Hotelling statistics is used to transfer two coefficients to a scalar. A simulation study is applied to compare the two methods in terms of the second type of error and average run length. Finally, two real examples are presented to demonstrate the applicability of the proposed chart. The first one is about semiconductors, and the second one is about pharmaceutical production processes. The performance of the methods is relatively similar. The maximum entropy plays an important role in correctly identifying differences in the pharmaceutical example, while linear regression did not correctly detect these changes.

翻译：在制造过程中，随时间监控过程对于减少时间和金钱浪费至关重要。休哈特图、累积和（CUSUM）图及指数加权移动平均（EWMA）图等常用控制图，适用于监控具有单一目标属性的过程，可应用于不同偏移范围的各种过程。然而，在某些情况下，过程质量由不同类型的轮廓特征来描述。本文旨在监控轮廓系数而非过程均值。为此，本文提出了两种同步监控简单线性轮廓截距和斜率的方法，并对其进行了比较：第一种为线性回归方法，第二种基于最大熵原理。利用T²霍特林统计量将两个系数转化为标量。通过仿真研究，从第二类误差和平均运行长度角度对两种方法进行了比较。最后，通过半导体制造和药品生产两个实际案例展示了所提控制图的适用性。两种方法的性能相对接近。在药品生产实例中，最大熵方法能正确识别差异，而线性回归则未能准确检测这些变化。