Statistical methods in process control and monitoring

过程控制和监测中的统计方法

• 批准号: 105613-2007
• 负责人: ViverosAguilera, Roman
• 金额: $ 1.09万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=451320
• 关键词: Statistical; methods; process; control; monitoring

Statistical process control has a wide range of applications in industry, services, administration and other areas. The statistical methods required depend on both the questions being asked and the features of the process quality data. The overall objective is to improve process performance. Control charts are the core methods used in statistical process control. Their development has largely focused on fault signatures characterized by a jump, up- or down-wards, in the mean of a quality characteristic in the manufacturing of a product or the offering of a service. Single-step slippages in the process controling variables are often responsible for these fault signatures. In practice, however, more often the mean of the quality characteristic of interest drifts away slowly, constantly changing over time. Linear, quadratic, exponential and sinusoidal trends are not uncommonly observed in process operation. Naturally these fault signatures are buried in noise, thus directly unobservable. Our proposal's main objective is to develop control chart methods to detect this variety of fault signatures. Specifically, the aim is to develop fault-sensitized control charts, that is, charts that are highly efficient in detecting a fault signature feared in advance. The recent control chart literature has focused on this type of problems. Another important objective is to develop charts for situations where the quality of interest does not necessarily follow a normal distribution. These situations are very common in reliability studies where the failure times of products or parts tend to be (positively) skewed, or in environmental studies where the concentrations of contaminants are in many cases log-normally distributed. We will apply sound statistical methods based on likelihood functions and score statistics to tackle these problems.

统计过程控制在工业、服务业、行政管理等领域有着广泛的应用。所需的统计方法取决于所提出的问题和过程质量数据的特征。总体目标是改进过程性能。控制图是统计过程控制的核心方法。它们的发展主要集中在故障特征上，其特征是在制造产品或提供服务的质量特征的平均值上向上或向下跳跃。过程控制变量中的单步滑移通常是造成这些故障特征的原因。然而，在实践中，兴趣的质量特征的平均值往往会慢慢地消失，随着时间的推移而不断变化。在过程操作中，线性、二次、指数和正弦趋势并不罕见。当然，这些故障信号被淹没在噪声中，因此无法直接观察到。我们建议的主要目标是开发控制图方法来检测各种故障特征。具体来说，目标是开发故障敏感控制图，即在检测提前担心的故障特征方面非常有效的图。最近的控制图文献集中在这类问题上。另一个重要目标是为兴趣质量不一定遵循正态分布的情况开发图表。这些情况在产品或部件的失效时间倾向于（正）偏斜的可靠性研究中非常常见，或者在污染物浓度在许多情况下呈对数正态分布的环境研究中非常常见。我们将运用基于似然函数和分数统计的统计方法来解决这些问题。”