首页> 外文期刊>Communications in statistics: theory and methods >Economic Design of VSI x-Bar Control Charts for Non Normally Distributed Data under Gamma (λ, 2) Failure Models
【24h】

Economic Design of VSI x-Bar Control Charts for Non Normally Distributed Data under Gamma (λ, 2) Failure Models

机译:VMAMA(λ,2)故障模型下非正常分布数据VSI X杆控制图表的经济设计

获取原文
获取原文并翻译 | 示例
           

摘要

This investigation develops the economic design of x-bar control charts with variable sampling interval (VSI) for non normal data under Gamma (X, 2) shock models. In the past, most of the economic design of control charts follows the Poisson process. However, pragmatically speaking, this process is usually not appropriate. Banerjee and Rahim (1988) presented a cost model which uses variable sampling intervals. This can be contrasted with sampling intervals of fixed length under a process-failure meclianism, which follows a Gamma (X, 1) model with an increased hazard rate. In a separate study by Al-Oraini and Rahim (2003), an economic statistical design of x-bar control charts with the assumption of the Gamma (λ, 2) failure mechanism was proposed. When the x bar chart is designed to monitor a manufacturing process, three parameters need to be determined via engineers or participators. These parameters are sample size, sampling interval between successive samples, and control limits. However, measurements in the subgroup are assumed to be normally distributed when designing control charts. Said assumption may be untenable. This article employs a numerical example to indicate the solution procedure and to implement sensitivity analysis. Also, the situation of non normal and normal in the Gamma (λ, 2) model was compared. Results in non normal assumption revealed the following: smaller sample size (n) is needed; the initial sampling interval (h_1) can become longer; the sampling interval (h_2) can become shorter; the control limit width (L) can become narrower; and the expected cost per hour (ECT) can likewise be reduced.
机译:本研究开发了具有可变采样间隔(VSI)的X-Bar控制图表的经济设计,用于伽马(x,2)冲击模型下的非正常数据。过去,控制图表的大多数经济设计遵循泊松过程。但是,务实说话,这个过程通常不合适。 Banerjee和Rahim(1988)提出了一种成本模型,它使用可变采样间隔。这可以与在流动故障的Meclianis中的固定长度的采样间隔形成对比,其遵循具有增加的危险率的伽马(x,1)模型。在Al-Otaini和Rahim(2003)的单独研究中,提出了一种假设γ(λ,2)故障机制的X杆控制图的经济统计设计。当X条形图旨在监控制造过程时,需要通过工程师或参与者确定三个参数。这些参数是样本大小,连续样本之间的采样间隔和控制限制。但是,假设在设计控制图表时通常分发子组中的测量。所述假设可能是站不住脚的。本文采用数字示例来指示解决方案程序并实现敏感性分析。而且,比较了γ(λ,2)模型中非正常和正常情况的情况。导致非正常假设揭示以下内容:需要较小的样品大小(n);初始采样间隔(H_1)可能变长;采样间隔(H_2)可以变短;控制极限宽度(L)可能变窄;并且同样可以减少每小时的预期成本(ECT)。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号