scholarly journals Joint Economic Design of CUSUM Control Chart and Age-Based Imperfect Preventive Maintenance Policy

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Yaping Li ◽  
Zhen Chen ◽  
Ershun Pan
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Preventive Maintenance ◽  
Economic Design ◽  
Cusum Chart ◽  
Maintenance Policy ◽  
Statistical Process ◽  
Decision Variables ◽  
Preventive Maintenance Policy

The close relationship between statistical process control and maintenance has attracted lots of researchers to focus on the jointly economic design of control chart (a main tool of statistical process control) and preventive maintenance policy, and much progress has been made in this field. However, in the existing literatures, the X- chart is used most, and other charts are rarely considered. In this paper, the economic design of CUSUM chart and age-based imperfect preventive maintenance policy is presented. The process is considered as a multiphase system, and a recursive algorithm is used to model each phase. Besides, a sampling policy under the non-Markovian deterioration assumption is employed, and an age-based imperfect preventive maintenance policy is used. An optimization model with the objective of minimizing the expected cost per unit time is constructed to obtain the near-optimal solution of decision variables: the age of the machine for maintenance, the number of age-based maintenances, sample size, sampling intervals, and the decision interval coefficient and reference value coefficient of CUSUM chart. The solution procedure of the model is provided. Also, sensitivity analysis is performed on the decision variables for each of the various parameters.

scholarly journals Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Nur Hidayah Mohd Razali ◽  
Lazim Abdullah ◽  
Zabidin Salleh ◽  
Ahmad Termimi Ab Ghani ◽  
Bee Wah Yap
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Cusum Chart ◽  
Statistical Process ◽  
Interval Type

Statistical process control is a method used for controlling processes in which causes of variations and correction actions can be observed. Control chart is one of the powerful tools of statistical process control that are used to control nonconforming products. Previous literature suggests that fuzzy charts are more sensitive than conventional control charts, and hence, they provide better quality and conformance of products. Nevertheless, some of the data used are more suitable to be presented in interval type-2 fuzzy numbers compared to type-1 fuzzy numbers as interval type-2 fuzzy numbers have more ability to capture uncertain and vague information. In this paper, we develop an interval type-2 fuzzy standardized cumulative sum (IT2F-SCUSUM) control chart and apply it to data of fertilizer production. This new approach combines the advantages of interval type-2 fuzzy numbers and standardized sample means which can control the variability. Twenty samples with a sample size of six were examined for testing the conformance. The proposed IT2F-SCUSUM control chart unveils that 15 samples are “out of control.” The results are also compared to the conventional CUSUM chart and type-1 fuzzy CUSUM chart. The conventional chart shows that 13 samples are “out of control.” In contrast, the type-1 fuzzy CUSUM chart shows that the process is “out of control” for 14 samples. In the analysis of average run length, the proposed IT2F-SCUSUM chart outperforms the other two CUSUM charts. Thus, we can conclude that the IT2F-SCUSUM chart is more sensitive and takes lesser number of observations to identify the shift in the process. The analyses suggest that the IT2F-SCUSUM chart is a promising tool in examining conformance of the quality of the fertilizer production.

Effectiveness Indices for Statistical Process Control Chart Performance

2019 ◽  
Vol 27 (1) ◽  
pp. 221-226
Author(s):  
Siyuan F. Yang ◽  
Wei-Ting K. Chien
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Statistical Process

scholarly journals Monitoring of mechanical sugarcane harvesting through control charts

2015 ◽  
Vol 35 (6) ◽  
pp. 1079-1092 ◽  
Author(s):  
Murilo A. Voltarelli ◽  
Rouverson P. da Silva ◽  
Cristiano Zerbato ◽  
Carla S. S. Paixão ◽  
Tiago de O. Tavares
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Grinding Wheel ◽  
Individual Values ◽  
Statistical Process ◽  
Minas Gerais State ◽  
Weighted Moving Average

ABSTRACT Statistical process control in mechanized farming is a new way to assess operation quality. In this sense, we aimed to compare three statistical process control tools applied to losses in sugarcane mechanical harvesting to determine the best control chart template for this quality indicator. Losses were daily monitored in farms located within Triângulo Mineiro region, in Minas Gerais state, Brazil. They were carried over a period of 70 days in the 2014 harvest. At the end of the evaluation period, 194 samples were collected in total for each type of loss. The control charts used were individual values chart, moving average and exponentially weighted moving average. The quality indicators assessed during sugarcane harvest were the following loss types: full grinding wheel, stumps, fixed piece, whole cane, chips, loose piece and total losses. The control chart of individual values is the best option for monitoring losses in sugarcane mechanical harvesting, as it is of easier result interpretation, in comparison to the others.

Statistical Process Control Technology Applied in ERP System

2011 ◽  
Vol 421 ◽  
pp. 461-464 ◽  
Author(s):  
Ying Ji Li ◽  
Wei Xi Ji
Keyword(s):  
Process Control ◽  
Production Process ◽  
Statistical Process Control ◽  
Control Chart ◽  
Nuclear Power ◽  
Analytical Data ◽  
Quality Data ◽  
Control Technology ◽  
Statistical Process ◽  
And Control

For the high and strict quality requirement in the manufacturing process of nuclear power parts, this paper is based on the combination of Statistical Process Control technology and the ERP quality management and control the production quality based on the control chart. PowerBuilder 9.0 and SQL Server2000 were used to design and develop the system while PowerBuilder 9.0 as front-end development tool and SQL Server2000 as back-end DBMS respectively. Firstly, collect the quality data of the production process (some important processes). Then, analysis these data and form control chart. Real-time monitor production process by the control charting to ensure the process is stability. Organic combination of SPC and ERP to improve and control the quality, not only enrich the analytical data of SPC, but also make up the ERP data to analysis and control quality data.

scholarly journals Use of ventilator associated pneumonia bundle and statistical process control chart to decrease VAP rate in Syria

2012 ◽  
Vol 2 (4) ◽  
pp. 79 ◽  
Author(s):  
Mazen Kherallah ◽  
Ali Marie ◽  
MonaN Mazloum ◽  
Ola Naes ◽  
AsemA Shamah ◽  
...  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Ventilator Associated Pneumonia ◽  
Statistical Process

scholarly journals Basic Developments of Quality Characteristics Monitoring

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Shahryar Sorooshian
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Quality Characteristics ◽  
Comprehensive Understanding ◽  
Statistical Process ◽  
Production Processes ◽  
Control Theories

Process control tools are a widely used approach in many operations and production processes. Process control chart ranks as one of the most important theories used in these disciplines. This paper reviewed the bias of quality characteristics monitoring. Specifically, this study tries to provide a comprehensive understanding of theories of process control. The text starts with a theoretical review of statistical process control theories and follows by a technical introduction to developed tools for process control.

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