scholarly journals Dynamic Risk Analysis in Metro Construction Using Statistical Process Control

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yichen Wang ◽  
Hong Zheng ◽  
Xinyue Lu
Keyword(s):  
Risk Analysis ◽  
Process Control ◽  
Dynamic Analysis ◽  
Statistical Process Control ◽  
Control Measures ◽  
Risk Level ◽  
Time Interval ◽  
Statistical Control ◽  
Statistical Process ◽  
And Control

Metro construction is normally carried out in complex engineering geological environment, so it can generate various risk events. In the process of metro construction, a scientific risk dynamic analysis is indispensable to reduce and control risks. In order to analyze the risk in metro construction more scientifically and reasonably, in this study, a new risk dynamic analysis method for metro construction is proposed using statistical process control. The method can analyse the risk level according to the process’s capacity index and identify the characteristics of risk variation according to the statistical control chart. The risk level and the characteristic of risks may vary with dynamical updating of monitoring data, so the conclusion of risk evaluation for a time interval can be drawn and corresponding safety measures can be ascertained. The method ushers statistical process control, so the random factors in risk evolution can be considered fully. Then, the method is applied to the risk analysis of shield construction under the Beijing-Tianjin intercity railway in Beijing Metro Line 8, a typical risk problem in the traffic construction. The variation of the risk level and the characteristic of risks can be evaluated reasonably because the dynamical randomness is considered. Moreover, whether risk control measures should be taken and what the effective measures are can be ascertained explicitly.

scholarly journals Improving Process Performance of Cotton Spinning by Using Statistical Process Control Techniques

2020 ◽  
pp. 15-22
Author(s):  
Dereje Girma ◽  
Omprakash Sahu
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Shop Floor ◽  
Statistical Control ◽  
Statistical Process ◽  
Process Capability Analysis ◽  
Capability Analysis ◽  
R Process ◽  
And Control

Identifying the presence and understanding the causes of process variability are key requirements for well controlled and quality manufacturing. This pilot study demonstrates the introduction of Statistical Process Control (SPC) methods to the spinning department of a textile manufacturing company. The methods employed included X Bar and R process control charts as well as process capability analysis. Investigation for 29 machine processes identified that none were in statistical control. Recommendations have been made for a repeat of the study using validated data together with practical application of SPC and control charts on the shop floor and extension to all processes within the factory.

scholarly journals Optimum Manpower Allocation in Health Care Using Statistical Process Control

2019 ◽  
pp. 1-9
Author(s):  
William E. Odinikuku ◽  
Jephtah A. Ikimi ◽  
Ikechukwu P. Onwuamaeze
Keyword(s):  
Health Care ◽  
Process Control ◽  
Statistical Process Control ◽  
Optimal Allocation ◽  
Manpower Planning ◽  
Production Costs ◽  
Statistical Control ◽  
Statistical Process ◽  
Planning Models ◽  
Allocation Process

In many countries manpower problems in the field of health care are regular items on the agenda of policy makers. To avoid mismatches between demand of care and supply of care on national and regional levels, manpower planning models and methods are used to determine adequate numbers of medical specialists to fulfill the future demand of care. Inadequate or inefficient allocation of manpower to various departments in an organization or workplace can lead to undesired outcomes which may include: down time, reduced productivity, workers fatigue, increased production costs, etc. As a result of the above stated problem, there is need to devise a statistical model that will ensure optimal allocation of manpower. In this study, the optimum allocation of two hundred and fifty two general nurses to fifteen wards at a hospital code named WCH located in South-South geopolitical zone, Nigeria was achieved using statistical process control. The study involved the analysis of data obtained from our hub of study for a period of two months. The C-chart was used to check if the process of allocation was in control or not. The result obtained from the study showed that the manpower allocation process was out of statistical control as the allocation of the children emergency ward was outside the upper control limit of the c-chart plot.

Excel Logistics Services

2017 ◽  
pp. 1-8
Author(s):  
Sunil Chopra
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Department Store ◽  
Distribution Center ◽  
Statistical Process ◽  
Logistics Services ◽  
And Control ◽  
Control Limits

Looks at the introduction of statistical process control (SPC) into a distribution center servicing a department store chain. Focuses on the receiving process in the distribution center and describes the introduction of SPC methodology. Discusses run charts, pareto diagrams, and control limits.To introduce statistical process control.

Standardization of CMM Fitting Algorithms and Development of Inspection Maps for Use in Statistical Process Control

2011 ◽  
Author(s):  
Neelakantan Mani ◽  
Jami J. Shah ◽  
Joseph K. Davidson
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Principal Component ◽  
Statistical Control ◽  
Statistical Process ◽  
Fitting Algorithm ◽  
Inspection Process ◽  
Feature Fitting ◽  
The Individual ◽  
Intelligent Methods

The choice of fitting algorithm in CMM metrology has often been based on mathematical convenience rather than the fundamental GD&T principles dictated by the ASME Y14.5 standard. Algorithms based on the least squares technique are mostly used for GD&T inspection and this wrong choice of fitting algorithm results in errors that are often overlooked and leads to deficiency in the inspection process. The efforts by organizations such as NIST and NPL and many other researchers to evaluate commercial CMM software were concerned with the mathematical correctness of the algorithms and developing efficient and intelligent methods to overcome the inherent difficulties associated with the mathematics of these algorithms. None of these works evaluate the ramifications of the choice of a particular fitting algorithm for a particular tolerance type. To illustrate the errors that can arise out of a wrong choice of fitting algorithm, a case study was done on a simple prismatic part with intentional variations and the algorithms that were employed in the software were reverse engineered. Based on the results of the experiments, a standardization of fitting algorithms is proposed in light of the definition provided in the standard and an interpretation of manual inspection methods. The standardized fitting algorithms developed for substitute feature fitting are then used to develop Inspection maps (i-Maps) for size, orientation and form tolerances that apply to planar feature types. A methodology for Statistical Process Control (SPC) using these i-Maps is developed by fitting the i-Maps for a batch of parts into the parent Tolerance Maps (T-Maps). Different methods of computing the i-Maps for a batch are explored such as the mean, standard deviations, computing the convex hull and doing a principal component analysis of the distribution of the individual parts. The control limits for the process and the SPC and process capability metrics are computed from inspection samples and the resulting i-Maps. Thus, a framework for statistical control of the manufacturing process is developed.

QUALIDADE NA COLHEITA MECANIZADA DE MILHO SEMEADO EM DIFERENTES VELOCIDADES

2016 ◽  
Vol 15 (3) ◽  
pp. 582 ◽  
Author(s):  
ANTONIO TASSIO SANTANA ORMOND ◽  
MURILO APARECIDO VOLTARELLI ◽  
CARLA SEGATTO STRINI PAIXÃO ◽  
ALINE SPAGGIARI ALCÂNTARA1 ◽  
ELIZABETH HARUNA KAZAMA ◽  
...  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Plant Density ◽  
Plant Population ◽  
Longitudinal Distribution ◽  
Statistical Control ◽  
Individual Values ◽  
Sowing Time ◽  
Statistical Process

RESUMO - As perdas na colheita podem estar relacionadas tanto a colhedora, como também a fatores ligados a cultura como: mau preparo do solo, densidade de plantas, inadequação da época de semeadura são alguns deles. O presente estudo objetivou determinar a influência da velocidade de semeadura no processo de colheita mecanizada de milho, por meio do controle de qualidade do processo. O experimento foi conduzido em Latossolo Vermelho, textura argilosa e relevo suave ondulado. O delineamento foi baseado na óptica do Controle Estatístico de ProcessoCEP, onde os dados foram coletados em pontos aleatórios em função do tempo. Os indicadores de qualidade avaliados foram divididos em parâmetros de semeadura (população de plantas e distribuição longitudinal de plântulas); e de colheita (Perdas de grãos e distribuição de palha) em função de seis velocidades de deslocamento (aproximadamente 2,0; 4,0; 6,0; 9,0; 10,0 e 12,0 Km.h-1). Os dados foram submetidos a análise descritiva para análise do comportamento. Como ferramentas do controle estatístico de processo utilizou-se, run charts ou gráfico sequencial e carta de controle de valores individuais para análise da qualidade do processo. A maior velocidade (V6) apresentou a maior variabilidade dos dados para todas as variáveis. A operação da colheita mecanizada de milho foi influenciada por fatores extrínsecos e intrínsecos a ela.Palavras-chave: Controle estatístico de processo, espaçamentos normais, perdas, população de plantas.QUALITY IN MECHANIZED HARVEST OF CORN SOWN IN DIFFERENT SPEEDSABSTRACT - The harvest losses may be associated to harvester as well as factors related to cultivation such as poor soil preparation, plant density, unsuitable sowing time. This study aimed to determine the effect of speed sowing in the mechanized harvest of corn, through the control of the quality of the process. The experiment was conducted in a clayey Oxisol and undulate relief. The design was based on the optics of the Statistical Process Control SPC, and the data were collected at random points in function of time. The quality indicators evaluated were divided into sowing parameters (plant population and longitudinal distribution of seedlings) and harvesting (loss of grain and straw distribution) in function of six displacement speeds (approximately 2.0, 4.0, 6.0, 9.0, 10.0 and 12.0 Km.h-1). The data were submitted to descriptive analysis for behavior analysis. As tools for the statistical control of the process, run charts or sequential graph were used, and control chart of individual values for analysis of the quality of the process. The highest speed (V6) showed the highest variability of the data for all variables. The operation of mechanized harvest of corn was influenced by extrinsic and intrinsic factors.Keywords: statistical process control, normal spacings, losses, plant population.

Variability Analysis by Statistical Control Process and Functional Data Analysis — Case of Study Applied to Power System Harmonics Assessment

2014 ◽  
Vol 615 ◽  
pp. 118-123 ◽  
Author(s):  
Joaquín Sancho ◽  
Jorge Pastor ◽  
Javier Martínez ◽  
Miguel Angel García
Keyword(s):  
Data Analysis ◽  
Process Control ◽  
Power Systems ◽  
Statistical Process Control ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Control Process ◽  
Industrial Applications ◽  
Statistical Control ◽  
Statistical Process

Functional data appear in a multitude of industrial applications and processes. However, in many cases at present, such data continue to be studied from the conventional standpoint based on Statistical Process Control (SPC), losing the capacity of analyzing different aspects over the time. In this study is presented a Statistical Control Process based on functional data analysis to identify outliers or special causes of variability of harmonics appearing in power systems which can negatively impact on quality of electricity supply. The results obtained from the functional approach are compared with those obtained with conventional Statistical Process Control that has been done firstly.

An Integrated Statistical Process Control and Wavelet Transformation Model for Detecting QRS Complexes in ECG Signals

2010 ◽  
Vol 1 (2) ◽  
pp. 1-20
Author(s):  
Wen-Hung Yang ◽  
Bernard C. Jiang
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Wavelet Transformation ◽  
Transformation Model ◽  
Change Points ◽  
Frequency Noise ◽  
Statistical Process ◽  
Ecg Signals ◽  
And Control ◽  
Control Limits

In this study, the authors propose an approach for detecting R-wave of electrocardiogram (ECG) signals. A statistical process control chart is successfully integrated with wavelet transformation (WT) to detect R-wave locations. This chart is a graphical display of the quality characteristic measured or computed from samples versus the sample number or time from the production line in a factory. This research performed WT at the signal preprocessing stage; the change points and control limits are then determined for each segment and the R-wave location is rechecked by spreading the points at the decision stage. The proposed procedures determine the change points and control limits for each segment. This method can be used to eliminate high-frequency noise, baseline shifts and artifacts from ECG signals, and R-waves can be effectively detected. In addition, there is flexibility in parameter value selection and robustness over wider noise ranges for the proposed QRS detection method.

CONTROLE ESTATÍSTICO DE PROCESSO (CEP) APLICADO NA COLHEITA MECANIZADA DO SORGO

2018 ◽  
Vol 17 (3) ◽  
pp. 490
Author(s):  
JOÃO PAULO BARRETO CUNHA ◽  
MILA SOUZA CASTRO ◽  
ANDERSON GOMIDE COSTA ◽  
MURILO MACHADO DE BARROS ◽  
TULIO ALMEIDA MACHADO ◽  
...  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Maximum Level ◽  
Statistical Process ◽  
Forage Sorghum ◽  
Randomized Design ◽  
Completely Randomized Design ◽  
And Control ◽  
The Ideal

RESUMO - A colheita, sendo uma das principais etapas no processo produtivo, precisa manter as perdas dentro de um controle aceitável para que seja possível atingir o máximo nível de qualidade e produtividade. No presente estudo, objetivou-se avaliar as perdas quantitativas durante a colheita mecanizada do sorgo forrageiro por meio do controle estatístico de processo (CEP). O experimento foi arranjado em delineamento inteiramente casualizado (DIC), em que foi realizada a análise de variância para a verificação do efeito significativo da declividade e da velocidade operacional nas perdas, e, quando significativos, foi submetida ao teste de comparação de médias de Tukey a 5% de significância. Cartas sequenciais e cartas de controle para valores individuais e de amplitude móveis foram utilizadas como ferramentas de controle estatístico de processo para verificar o efeito da velocidade operacional nas perdas. Com base nos resultados obtidos é possível indicar que a faixa de velocidade operacional de 4 a 5 km h-1 apresentou a menor variação dos dados, não apresentando nenhum ponto fora do limite de controle, o que lhe conferiu a condição de faixa ideal para colheita. Com base na análise estatística houve maiores perdas no transporte à medida que se aumenta a faixa de declividade do terreno.Palavras-chave: colheita mecanizada, forragicultura, carta de controle, velocidade operacional. STATISTICAL PROCESS CONTROL (SPC) APPLIED IN THE MECHANIZED HARVEST OF SORGHUM  ABSTRACT - Harvesting is one of the main steps in the production process and it is necessary to keep the losses under control in order to reach the maximum level of quality and productivity. The present study aimed to evaluate the quantitative losses during the mechanized harvesting of forage sorghum using the statistical process control (SPC). The experiment was arranged in a completely randomized design (DIC), and analysis of variance was performed to verify the significant effect of declivity and operational velocity on losses, and the significant was submitted to the Tukey test at 5% significance. Sequential charts and control charts for individual and mobile amplitude values, composed of upper and lower control and average limits, were used as statistical process control tools to verify the effect of operational speed on losses. Based on the results obtained it is possible to indicate that the operational velocity range from 4 to 5 km h-1 presented the lowest variation of data, presenting no point outside of the control limit, being the ideal range for harvest. The statistical analysis showed higher losses in transportation as the slope of the terrain increased.Keywords: Mechanized harvesting, forage farming, control charts, operational speeds.

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.

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