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A neural network approach for multi-attribute process control with comparison of two current techniques and guidelines for practical use.

机译：一种神经网络方法，用于多属性过程控制，并比较了两种当前技术和实际使用指南。

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摘要

Both manufacturing and service industries deal with quality characteristics, which include not only variables but attributes as well. In the area of Quality Control there has been substantial research in the area of correlated variables (i.e. multivariate control charts); however, little work has been done in the area of correlated attributes. To control product or service quality of a multi-attribute process, several issues arise. A high number of false alarms (Type I error) occur and the probability of not detecting defects increases when the process is monitored by a set of uni-attribute control charts. Furthermore, plotting and monitoring several uni-attribute control charts makes additional work for quality personnel.; To date, a standard method for constructing a multi-attribute control chart has not been fully evaluated. In this research, three different techniques for simultaneously monitoring correlated process attributes have been compared: the normal approximation, the multivariate np-chart (MNP chart), and a new proposed Neural Network technique. The normal approximation is a technique of approximating multivariate binomial and Poisson distributions as normal distributions. The multivariate np chart (MNP chart) is base on traditional Shewhart control charts designed for multiple attribute processes. Finally, a Backpropagation Neural Network technique has been developed for this research. Each technique should be capable of identifying an out-of-control process while considering all correlated attributes simultaneously.; To compare the three techniques an experiment was designed for two correlated attributes. The experiment consisted of three levels of proportion nonconforming p, three values of the correlation matrix, three sample sizes, and three magnitudes of shift of proportion nonconforming in either the positive or negative direction. Each technique was evaluated based on average run length and the number of replications of correctly identified given the direction of shifts (positive or negative). The resulting performances for all three techniques at their varied process conditions were presented and compared.; From this study, it has observed that no one technique outperforms the other two techniques for all process conditions. In order to select a suitable technique, a user must be knowledgeable about the nature of their process and understand the risks associated with committing Type I and II errors. Guidelines for how to best select and use multi-attribute process control techniques are provided.
机译：制造业和服务业都处理质量特征，不仅包括变量，还包括属性。在质量控制领域，对相关变量（即多变量控制图）进行了大量研究；但是，在相关属性领域所做的工作很少。为了控制多属性过程的产品或服务质量，出现了几个问题。当通过一组单属性控制图监视过程时，会发生大量的错误警报（I类错误），并且未检测到缺陷的可能性也会增加。此外，绘制和监视多个单属性控制图会使质量人员付出更多工作。迄今为止，构建多属性控制图的标准方法尚未得到充分评估。在这项研究中，比较了三种同时监视相关过程属性的不同技术：正态近似，多元np图（MNP图）和新提出的神经网络技术。正态近似是将多元二项式和泊松分布近似为正态分布的技术。多元np图表（MNP图表）基于为多属性过程设计的传统Shewhart控制图。最后，为该研究开发了反向传播神经网络技术。每种技术都应能够识别失控过程，同时考虑所有相关属性。为了比较这三种技术，针对两个相关属性设计了一个实验。实验包括三个级别的比例不合格p，三个相关矩阵值，三个样本大小以及三个比例不合格的正向或负向移动幅度。根据平均运行时间和给定的移动方向（正向或负向）正确识别的重复次数评估每种技术。介绍并比较了这三种技术在不同工艺条件下的最终性能。从这项研究中，可以观察到在所有工艺条件下，没有一种技术优于其他两种技术。为了选择合适的技术，用户必须了解其过程的性质，并了解与实施I型和II型错误有关的风险。提供了有关如何最佳选择和使用多属性过程控制技术的指南。

著录项

  • 作者

    Larpkiattaworn, Siripen.;

  • 作者单位

    University of Pittsburgh.;

  • 授予单位 University of Pittsburgh.;
  • 学科 Engineering Industrial.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 186 p.
  • 总页数 186
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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