نوع مقاله : پژوهشی
نویسندگان
1 گروه مهندسی صنایع. دانشگاه یزد
2 گروه مهندسی صنایع، دانشگاه یزد
3 دانشکده مهندسی صنایع، دانشکدگان فنی، دانشگاه تهران
4 گروه مهندسی صنایع دانشگاه قم
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
Thinking that any deviation should be recognized as soon as possible will often be impractical. Despite the existence of numerous assignable causes in the process, their effects may be so small and minor against the permissible tolerance. Identifying them seems uneconomical from practical sights. If the process reaches a high level of capability, the production may be acceptable even though assignable causes befall. Since customers’ expectations will not be affected in this case, it is not economical to stop the process. By considering the level of specifications, some changes in the average can be allowed. Dividing the conditions of the monitored process into just black and white can be simplistic. In such cases, traditional control charts with two zones are not applicable. By defining the zone of indifference, permissible deviations can be tolerated. For such a situation, Acceptance Control Chart (ACC) is developed based on three zones. Suppose that a statistically assignable cause is detected using the traditional control charts; however, no signal is observed by the ACC. Thus, this change does not result in a nonconforming output, and there is no need to stop production since no operational loss occurs. The most important assumptions of the ACC are the normality and independence of the monitored data. In some industrial/non-industrial processes (e.g., continuous production processes, financial processes, network monitoring, and environmental phenomena), serial correlation can be extracted among samples, which violates the assumption of independence. Autocorrelation reduces the performance of traditional control charts by producing frequent false signals in the in-control state or makes them respond slowly to the detection of the out-of-control state. The main purpose of this study is to develop an ACC for monitoring the data of the most widely used autocorrelated process, namely the first-order autoregressive process AR(1). In this regard, two types of ACC are extended for the residuals of AR(1) processes. After evaluating the performance of monitoring methods using the average run length (ARL), it is found that the proposed EWMA chart has better results. Moreover, the economic-statistical design of the proposed chart is carried out at a lower cost.
کلیدواژهها [English]
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