An individual-specific fall detection model based on the statistical process control chart

2014 ◽  
Vol 64 ◽  
pp. 13-21 ◽  
Author(s):  
Xinyao Hu ◽  
Xingda Qu
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Fall Detection ◽  
Statistical Process ◽  
Detection Model ◽  
Model Based

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
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