Bibliografie tematiche / Statistical Process control chart

Letteratura scientifica selezionata sul tema "Statistical Process control chart"

Autore: Grafiati

Pubblicato: 8 giugno 2024

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Articoli di riviste sul tema "Statistical Process control chart":

Wang, Hai Yu. "Statistical Process Control on Time Delay Feedback Adjustment Process". Advanced Materials Research 211-212 (febbraio 2011): 305–9. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.305.

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Control chart can be designed to quickly detect small shifts in the mean of a sequence of independent normal observations. But this chart cannot perform well for autocorrelated process. The main goal of this article is to suggest a control chart method using to monitoring process with different time delay feedback controlled processes. A quality control model based on delay feedback controlled processes is set up. And the calculating method of average run length of control charts based on process output and control action of multiple steps delay MMSE feedback controlled processes is provided to evaluate control charts performance. A simple example is used to illustrate the procedure of this approach.

Benková, Marta, Dagmar Bednárová, Gabriela Bogdanovská e Marcela Pavlíčková. "Use of Statistical Process Control for Coking Time Monitoring". Mathematics 11, n. 16 (8 agosto 2023): 3444. http://dx.doi.org/10.3390/math11163444.

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Technical and technological developments in recent decades have stimulated the rapid development of methods and tools in the field of statistical process quality control, which also includes control charts. The principle of control charts defined by Dr. W. Shewhart has been known for more than 100 years. Since then, they have been used in many industries to monitor and control processes. This paper aims to assess the possibilities of use and the selection of the most suitable type of control chart for monitoring the quality of a process depending on its nature. This tool should help operators in monitoring coking time, which is one of the important control variables affecting the quality of coke production. The autoregressive nature of the variable being monitored was considered when selecting a suitable control chart from the group of options considered. In addition to the three traditional types of control charts (Shewhart’s, CUSUM, and EWMA), which were applied to the residuals of individual values of different types of ARIMA models, various statistical tests, and plots, a dynamic EWMA control chart was also used. Its advantage over traditional control charts applied to residuals is that it works with directly measured coking time data. This chart is intended to serve as a method to monitor the process. Its role is only to alert the process operator to the occurrence of problems with the length of the coking time.

Rashid, Kawa. "Design Tukey’s Control Chart and mix with CUSUM Control Chart". Journal of Zankoy Sulaimani - Part A 24, n. 1 (20 giugno 2022): 55–66. http://dx.doi.org/10.17656/jzs.10869.

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Statistical process control is a collection of valuable tools for detecting alteration in a process. It has wide application in many areas field and other fields where variation is being monitored. The variation may be a natural cause variation or a particular cause variation. Statistical process control deals with the monitoring process to detect disturbances in the process. These disturbances may be from the process mean or variance. This study proposes efficient charts for detecting early shifts in dispersion parameters by applying the Fast Initial Response feature. We propose and compare the performance of different cumulative sum (CUSUM)control charts for phase II monitoring of location based on mean and median. The (CUSUM) control chart, which is a method of data analysis based on John Tukey's principles control chart (TCC), is used to compare the proposed charts with their existing counterparts is used to evaluate new charts to existing charts using performance measures such as average run length, the standard deviation of run length, additional quadratic loss, relative average run length, and performance comparison . The proposed charts detect early shifts in the process dispersion faster and have better overall. This article is a similar effort to design an improved charting structure in the form of mixed or using Tukey -CUSUM chart together, to show the process control chart., and drawing the Average Run Length ARL value.

Avakh Darestani, Soroush, e Mina Nasiri. "Statistical process control". International Journal of Quality & Reliability Management 33, n. 1 (31 dicembre 2015): 2–24. http://dx.doi.org/10.1108/ijqrm-08-2013-0130.

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Purpose – In this context, process capability indices (PCI) reveal the process zones base on specification limits (SLs). Most of the research on control charts assumed certain data. However, to measure quality characteristic, practitioners sometimes face with uncertain and linguistic variables. Fuzzy theory is one of the most applicable tools which academia has employed to deal with uncertainty. The paper aims to discuss these issues. Design/methodology/approach – In this investigation, first, fuzzy and S control chart has been developed and second, the fuzzy formulation of the PCIs such as C pm ,C pmu ,C pml , C pmk , P p , P pl , P pu , P pk are constructed when SLs and measurements are at both triangular fuzzy numbers (TFNs) and trapezoidal fuzzy numbers (TrFNs) stages. Findings – The results show that using fuzzy make more flexibility and sense on recognition of out-of-control warnings. Research limitations/implications – For further research, the PCIs for non-normal data can be conducted based on TFN and TrFN. Practical implications – The application case is related to a piston company in Konya’s industry area. Originality/value – In the previous researches, for calculating C p , C pk , C pm and C pmk indices, the base approach was calculate standard deviation for a short term variation. For calculating these indices, the variation between subgroups are being ignored. Therefore, P p and P pk indices solved this fault by mentioning long term and short term variations. Therefore these two indices calculate the actual process capability.

Aslam, Muhammad, Nasrullah Khan e Muhammad Khan. "Monitoring the Variability in the Process Using Neutrosophic Statistical Interval Method". Symmetry 10, n. 11 (1 novembre 2018): 562. http://dx.doi.org/10.3390/sym10110562.

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Existing variance control charts are designed under the assumptions that no uncertain, fuzzy and imprecise observations or parameters are in the population or the sample. Neutrosophic statistics, which is the extension of classical statistics, has been widely used when there is uncertainty in the data. In this paper, we will originally design S 2 control chart under the neutrosophic interval methods. The complete structure of the neutrosophic S 2 control chart will be given. The necessary measures of neutrosophic S 2 will be given. The neutrosophic coefficient of S 2 control chart will be determined through the neutrosophic algorithm. Some tables are given for practical use. The efficiency of the proposed control chart is shown over the S 2 control chart designed under the classical statistics in neutrosophic average run length (NARL). A real example is also added to illustrate the proposed control chart. From the comparison in the simulation study and case study, it is concluded that the proposed control chart performs better than the existing control chart under uncertainty.

Vicentin, Damaris Serigatto, Brena Bezerra Silva, Isabela Piccirillo, Fernanda Campos Bueno e Pedro Carlos Oprime. "Monitoring process control chart with finite mixture probability distribution". International Journal of Quality & Reliability Management 35, n. 2 (5 febbraio 2018): 335–53. http://dx.doi.org/10.1108/ijqrm-11-2016-0196.

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Purpose The purpose of this paper is to develop a monitoring multiple-stream processes control chart with a finite mixture of probability distributions in the manufacture industry. Design/methodology/approach Data were collected during production of a wheat-based dough in a food industry and the control charts were developed with these steps: to collect the master sample from different production batches; to verify, by graphical methods, the quantity and the characterization of the number of mixing probability distributions in the production batch; to adjust the theoretical model of probability distribution of each subpopulation in the production batch; to make a statistical model considering the mixture distribution of probability and assuming that the statistical parameters are unknown; to determine control limits; and to compare the mixture chart with traditional control chart. Findings A graph was developed for monitoring a multi-stream process composed by some parameters considered in its calculation with similar efficiency to the traditional control chart. Originality/value The control chart can be an efficient tool for customers that receive product batches continuously from a supplier and need to monitor statistically the critical quality parameters.

Thepvongs, Somchart, e Brian M. Kleiner. "Inspection in Process Control". Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, n. 16 (ottobre 1998): 1170–74. http://dx.doi.org/10.1177/154193129804201619.

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Consistent with the precepts of total quality control and total quality management, there has been a resource shift from incoming and outgoing inspection processes to statistical quality control of processes. Furthermore, process control operators are responsible for their own quality, necessitating the in-process inspection of components. This study treated the statistical process control task of “searching” control charts for out-of-control conditions as an inspection task and applied the Theory of Signal Detection to better understand this behavior and improve performance. Twelve subjects participated in a research study to examine how the portrayal of control chart information affected signal detection theory measures. The type of display did not have a significant effect on the sensitivity and response criterion of subjects. These results are discussed in terms of the applicability of Signal Detection Theory in control chart decision making as well as implications on display design.

Omar, M. Hafidz, Sheikh Y. Arafat, M. Pear Hossain e Muhammad Riaz. "Inverse Maxwell Distribution and Statistical Process Control: An Efficient Approach for Monitoring Positively Skewed Process". Symmetry 13, n. 2 (25 gennaio 2021): 189. http://dx.doi.org/10.3390/sym13020189.

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(1) Background: The literature discusses the inverse Maxwell distribution theoretically without application. Control charting is promising, but needs development for inverse Maxwell processes. (2) Methods: Thus, we develop the VIM control chart for monitoring the inverse Maxwell scale parameter and studied its statistical properties. The chart’s performance is evaluated using power curves and run length properties. (3) Results: Further, we use simulated data to compare the shift detection capability of our chart with Weibull, gamma, and lognormal charts. (4) Conclusion: The analysis demonstrates our chart’s efficiency for monitoring skewed processes. Finally, we apply our chart for monitoring real world lifetimes of car brake pads.

Gloi, Aime M., Vladimir Stankovich, Stanley Mayas e Benjamin Rodriguez. "Statistical process control: machine performance check output variation". International Journal of Research in Medical Sciences 11, n. 7 (30 giugno 2023): 2365–71. http://dx.doi.org/10.18203/2320-6012.ijrms20232072.

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Background: The aim of this study was to illustrate and evaluate the use of different statistical process control (SPC) aspects to examine linear accelerator daily output variation through machine performance check (MPC) over a month. Methods: MPC daily output data were obtained over a month after AAPM TG-51 were performed. Baseline data were set, and subsequent data were conducted through SPC. The Shewhart chart was used to determine the upper and lower control limits, whereas CUSUM for subtle changes. Results: The upper and lower control limits obtained via SPC analysis of the MPC data were found to fall within AAPM Task Group 142 guidelines. MPC output variation data were within ±3% of their action limits values and were within 1% over thirty days of data. The process capability ratio and process acceptability ratio, Cp and Cpk values were ≥2 for all energies. Potential undetected deviations were captured by the CUSUM chart for photons and electrons beam energy. Conclusions: Control charts were found to be useful in terms of detecting changes in MPC output.

Niezgoda, Janusz. "The Use of Statistical Process Control Tools for Analysing Financial Statements". Folia Oeconomica Stetinensia 17, n. 1 (27 giugno 2017): 129–37. http://dx.doi.org/10.1515/foli-2017-0010.

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Abstract This article presents the proposed application of one type of the modified Shewhart control charts in the monitoring of changes in the aggregated level of financial ratios. The control chart x̅ has been used as a basis of analysis. The examined variable from the sample in the mentioned chart is the arithmetic mean. The author proposes to substitute it with a synthetic measure that is determined and based on the selected ratios. As the ratios mentioned above, are expressed in different units and characters, the author applies standardisation. The results of selected comparative analyses have been presented for both bankrupts and non-bankrupts. They indicate the possibility of using control charts as an auxiliary tool in financial analyses.

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Tesi sul tema "Statistical Process control chart":

SIMOES, BRUNO FRANCISCO TEIXEIRA. "EWMA CHART WITH ADAPTIVE SMOOTHING CONSTANT FOR STATISTICAL PROCESS CONTROL". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8189@1.

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Abstract (sommario):

COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
Este trabalho propõe um gráfico de controle EWMA para observações individuais ou médias amostrais, com a constante de amortecimento variando entre dois valores de acordo com o valor mais recente da estatística EWMA, para obter detecção mais rápida de alterações pequenas a moderadas na média do processo, e sem a complexidade operacional apresentada por outros esquemas adaptativos, pois o tamanho da amostra e o intervalo de amostragem são mantidos fixos. Já existe um outro trabalho propondo a variação da constante de amortecimento dos gráficos EWMA, mas com base em outro critério: Capizzi e Masarotto (2003). O esquema EWMA adaptativo foi combinado com limites de Shewhart para os valores individuais (ou médias amostrais), para acelerar a detecção de grandes deslocamentos da média do processo, também sem aumento da complexidade operacional. Os NMA1´s - números esperados de amostras até um sinal verdadeiro - foram calculados por um método de aproximação numérica usando um modelo matemático por cadeias de Markov, e comparados com os do esquema EWMA tradicional (com parâmetros fixos) e com os do esquema adaptativo de Capizzi e Masarotto (2003). O esquema proposto tende a fornecer NMA1´s menores para alterações na média acima de 1,0 desvio-padrão, e o esquema de Capizzi e Masarotto (2003) tende a fornecer NMA1´s menores para pequenas alterações. Ambos os esquemas possuem melhor desempenho que o gráfico EWMA com parâmetros fixos. Uma vantagem que pode se tornar decisiva para a adoção do esquema proposto é a simplicidade dos cálculos requeridos para o monitoramento.
This work proposes an EWMA process control chart for individual observations or subgroup averages, in which the smoothing constant varies between two values according to the most recent value of the EWMA statistic, in order to achieve faster detection of small to moderate shifts in the process mean, and without the operational complexities presented by other adaptive schemes, since its sample size and sampling interval do not vary. There is one other work proposing the adaptive variation of the smoothing constant of EWMA charts, but based on a different criterion: Capizzi and Masarotto (2003). The adaptive EWMA scheme was combined with Shewhart limits for the individual values (or subgroup averages), to enhance its sensitivity to large shifts, again with no extra operational burden. The out-of-control average run lengths (ARL1´s) were calculated through a numerical approximation method based on a Markov chain model. The ARL1´s were compared of the proposed scheme, of the traditional (fixed parameter) EWMA chart and of Capizzi and Masarottos´s adaptive EWMA scheme. The proposed scheme generally provides the shortest ARL1´s for shifts in the mean above one standard deviation, and Capizzi and Masarotto´s scheme tends to outperform it for smaller shifts. Both schemes perform better than the fixed parameter EWMA. An advantage that can become decisive for the adoption of the proposed scheme is the simplicity of the calculations required for the monitoring.

Sundholm, Per. "Statistical Process Control for the Sawmill Industry". Thesis, Umeå universitet, Institutionen för fysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-112046.

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In the sawmill industry, it can be very proﬁtable to monitor the dimensions of sawn boards so that operators quickly can detect errors and take cor-rective action. In this master’s thesis project, Statistical Process Control (SPC) methods have been implemented to achieve this. SPC is a set of statistical methods whose purpose is to minimize the variations in an in-dustrial process. In particular, the SPC method used here is the control chart, which with an upper and lower control limit quantiﬁes the bounds of natural variation. To ﬁnd the most suitable control chart, ﬁve control charts monitoring the process mean, and two monitoring process variability were tested with help of both a simulation study and an empirical evaluation. The result of the evaluation was that the ”Average Moving Range” chart was regarded the most suitable for changes in process mean, and the Range chart was regarded as the best at detecting changes in process variability. Both charts are constructed for individual boards and not subgroups of boards (as is more common) due to compatibility reasons with the existing measurement practice. The two methods were deemed to be quite able to detect process changes, but some results indicate that the methods might work better for double arbour saw lines than single arbour ones.
Det kan vara mycket lönsamt för sågverk att övervaka mått på plankor så att personal snabbt kan hitta och åtgärda fel som uppstår i processen. I det syftet har det här masterarbetet gått ut på att implementera statistisk processkontroll (SPC) för råmåttkontroll på sågverk. SPC är en mängd olika statistiska metoder vars syfte är att minimera spridningen i en tillverkningsprocess. Den metod som är i speciellt focus i det här arbetet är det så kallade styrdiagrammet som med en övre och undre gräns kvantiﬁerar hur stor den naturligt förekommande spridningen är. För att ﬁnna det mest lämpade styrdiagrammet utvärderades fem styrdiagram som övervakar processens medelvärde och två styrdiagram som övervakar processens spridning. Denna utvärdering bestod både av en simuleringsstudie och tester gjorda för empiriskt data. Utvärderingen resulterade i att det så kallade ”Average Moving Range” diagrammet rekommenderades för övervakning av medelvärde och ett räckviddsstyrdiagram rekommenderades för spridningen. Båda styrdiagrammen konstruerades för enskilda plankor och inte för stickprov av ﬂera plankor (vilket är vanligare) på grund av kompatibelitetsskäl med gängse mätmetodik. De båda metoderna ansågs vara ganska bra på att upptäcka processförändringar men vissa resultat tyder på att metoderna kanske fungerar bättre för sågverk med mötande klingor än enaxliga sågverk.

Ouyang, Jintao. "Cumulative quantity control chart and maintenance strategies for industrial processes". Thesis, Click to view the E-thesis via HKUTO, 2004. http://sunzi.lib.hku.hk/hkuto/record/B31121184.

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el, homani Abdellatif. "NOVEL APPROACHES FOR STATISTICAL PROCESS CONTROL CHARTS PATTERN RECOGNITION". OpenSIUC, 2010. https://opensiuc.lib.siu.edu/dissertations/152.

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Fast and accurate recognition of the Statistical Control Chart Patterns (SPCCP) is significant for supervising manufacturing processes to accomplish better control and to make high value products. SPCCP can display eight kinds of patterns: normal, stratification, systematic, increasing trend, decreasing trend, up shift, down shift and cyclic. With the exception of the natural pattern, all other patterns indicate that the supervised manufacturing process is not performing properly and actions need to be taken to correct the problems. This research proposes new approaches, neural networks and neural-fuzzy systems, to the (SPCCP) recognition. This dissertation also investigates the use of features extracted from statistical analysis for simple patterns, and wavelet analysis for concurrent patterns as the components of the input vectors. Results based on simulated data show that the proposed approaches perform better than conventional approaches. Our work concluded that the extracted features improve the performance of the proposed recognizer systems.

Kirbas, Serkan. "An Assessment And Analysis Tool For Statistical Process Control Of Software Processes". Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608196/index.pdf.

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Statistical process control (SPC) which includes very powerful techniques used in other mature engineering disciplines for providing process control is not used by many software organizations. In software engineering domain, SPC is currently utilized only by organizations which have high maturity levels according to the process improvement models like CMM, ISO/IEC 15504 and CMMI. Guidelines and software tools to implement SPC techniques should be developed for effective use and dissemination of SPC especially for low maturity organizations. In this thesis, a software tool (SPC-AAT) which we developed to assess the suitability of software processes and metrics for SPC and use of SPC tools is presented. With SPC-AAT, we aim to ease and enhance application of SPC especially for emergent and low maturity organizations. Control charts, histograms, bar charts and pareto charts are the supported SPC tools for this purpose. We also explained the validation of the tool over two processes of a software organization in three case studies.

Chin, Chang-Ho. "Optimal filter design approaches to statistical process control for autocorrelated processes". Texas A&M University, 2004. http://hdl.handle.net/1969.1/2776.

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Statistical Process Control (SPC), and in particular control charting, is widely used to achieve and maintain control of various processes in manufacturing. A control chart is a graphical display that plots quality characteristics versus the sample number or the time line. Interest in effective implementation of control charts for autocorrelated processes has increased in recent years. However, because of the complexities involved, few systematic design approaches have thus far been developed. Many control charting methods can be viewed as the charting of the output of a linear filter applied to the process data. In this dissertation, we generalize the concept of linear filters for control charts and propose new control charting schemes, the general linear filter (GLF) and the 2nd-order linear filter, based on the generalization. In addition, their optimal design methodologies are developed, where the filter parameters are optimally selected to minimize the out-of-control Average Run Length (ARL) while constraining the in-control ARL to some desired value. The optimal linear filters are compared with other methods in terms of ARL performance, and a number of their interesting characteristics are discussed for various types of mean shifts (step, spike, sinusoidal) and various ARMA process models (i.i.d., AR(1), ARMA(1,1)). Also, in this work, a new discretization approach for substantially reducing the computational time and memory use for the Markov chain method of calculating the ARL is proposed. Finally, a gradient-based optimization strategy for searching optimal linear filters is illustrated.

Wilson, Sara R. "Control Charts with Missing Observations". Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/37623.

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Traditional control charts for process monitoring are based on taking samples from the process at regular time intervals. However, it is often possible in practice for observations, and even entire samples, to be missing. This dissertation investigates missing observations in Exponentially Weighted Moving Average (EWMA) and Multivariate EWMA (MEWMA) control charts. The standardized sample mean is used since this adjusts the sample mean for the fact that part of the sample may be missing. It also allows for constant control limits even though the sample size varies randomly. When complete samples are missing, the weights between samples should also be adjusted. In the univariate case, three approaches for adjusting the weights of the EWMA control statistic are investigated: (1) ignoring missing samples; (2) adding the weights from previous consecutive missing samples to the current sample; and (3) increasing the weights of non-missing samples in proportion, so that the weights sum to one. Integral equation and Markov chain methods are developed to find and compare the statistical properties of these charts. The EI chart, which adjusts the weights by ignoring the missing samples, has the best overall performance. The multivariate case in which information on some of the variables is missing is also examined using MEWMA charts. Two methods for adjusting the weights of the MEWMA control statistic are investigated and compared using simulation: (1) ignoring all the data at a sampling point if the data for at least one variable is missing; and (2) using the previous EWMA value for any variable for which all the data are missing. Both of these methods are examined when the in-control covariance matrix is adjusted at each sampling point to account for missing observations, and when it is not adjusted. The MS control chart, which uses the previous value of the EWMA statistic for a variable if all of the data for that variable is missing at a sampling point, provides the best overall performance. The in-control covariance matrix needs to be adjusted at each sampling point, unless the variables are independent or only weakly correlated.
Ph. D.

Park, Yongro. "A statistical process control approach for network intrusion detection". Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6835.

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Intrusion detection systems (IDS) have a vital role in protecting computer networks and information systems. In this thesis we applied an SPC monitoring concept to a certain type of traffic data in order to detect a network intrusion. We developed a general SPC intrusion detection approach and described it and the source and the preparation of data used in this thesis. We extracted sample data sets that represent various situations, calculated event intensities for each situation, and stored these sample data sets in the data repository for use in future research. A regular batch mean chart was used to remove the sample datas inherent 60-second cycles. However, this proved too slow in detecting a signal because the regular batch mean chart only monitored the statistic at the end of the batch. To gain faster results, a modified batch mean (MBM) chart was developed that met this goal. Subsequently, we developed the Modified Batch Mean Shewhart chart, the Modified Batch Mean Cusum chart, and the Modified Batch Mean EWMA chart and analyzed the performances of each one on simulated data. The simulation studies showed that the MBM charts perform especially well with large signals ?the type of signal typically associated with a DOS intrusion. The MBM Charts can be applied two ways: by using actual control limits or by using robust control limits. The actual control limits must be determined by simulation, but the robust control limits require nothing more than the use of the recommended limits. The robust MBM Shewhart chart was developed based on choosing appropriate values based on batch size. The robust MBM Cusum chart and robust MBM EWMA chart were developed on choosing appropriate values of charting parameters.

Liu, Qingyun. "Statistical Process Control for the Fairness of Network Resource Distribution". FIU Digital Commons, 2011. http://digitalcommons.fiu.edu/etd/518.

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The purpose of this research is to develop a statistical method to monitor the fairness of network resource distribution. The newly developed fairness score function allows users to have the same or different priority levels. Especially, this function possesses all the necessary properties required as a quality characteristic for the purpose of statistical process control. The main objective is to find the critical values for the statistical test. Monte Carlo simulation is used to find the critical values. When the users have the same priority level, a table of the critical values is given for different sample sizes and different significance levels. When the users have different priority levels, it is difficult to generate a similar table since the users’ priority levels vary. Therefore, the critical values are computed for given priority levels. In both cases, an example is given to demonstrate the approach developed in this study.

Šváchová, Mariana. "Určování způsobilosti a stability vybraného technického procesu". Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-417798.

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This diploma thesis deals with the evaluation of the capability of a specific production process. The theoretical part of the work contains a description of statistical process control, types of control charts and evaluation of process capability. The practical part is focused on evaluating the capability of a specific process. The method of dataset collection is described at first, then this data are analyzed and the capability of this process is evaluated.

Più fonti

Libri sul tema "Statistical Process control chart":

Wheeler, Donald J. Advanced topics in statistical process control: The power of Shewhart's charts. Knoxville, Tenn: SPC Press, 1995.

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Meijer, Rob R. The use of statistical process control-charts for person-fit analysis in computerized adaptive testing. Newtown, Pa: Law School Admission Council, 2003.

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Wise, Stephen A. Innovative control charting: Practical SPC solutions for today's manufacturing environment. Milwaukee, Wis: ASQ Quality Press, 1998.

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Oakland, John S. Statistical process control. 5^a ed. Oxford: Butterworth-Heinemann, 2003.

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Stephen, Mundwiller. Statistical Process Control. Boca Raton : CRC Press, Taylor & Francis Group, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781351248518.

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Oakland. Statistical Process Control. 7th edition. | Abingdon, Oxon ; New York, NY : Routledge, 2019.: Routledge, 2018. http://dx.doi.org/10.4324/9781315160511.

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Wetherill, G. Barrie, e Don W. Brown. Statistical Process Control. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-2949-5.

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Oakland, John S. Statistical process control. 6^a ed. Burlington, MA: Butterworth-Heinemann, 2008.

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Liu, Huamin. Statistical process control. Birmingham: University of Birmingham, 1988.

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Oakland, John S. Statistical process control. 3^a ed. Oxford: Butterworth-Heinemann, 1996.

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Capitoli di libri sul tema "Statistical Process control chart":

Oakland, John, e Robert Oakland. "Cumulative sum (cusum) charts". In Statistical Process Control, 218–43. 7^a ed. 7th edition. | Abingdon, Oxon ; New York, NY : Routledge, 2019.: Routledge, 2018. http://dx.doi.org/10.4324/9781315160511-9.

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Wetherill, G. Barrie, e Don W. Brown. "Basic Shewhart control charts for continuous variables". In Statistical Process Control, 85–113. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-2949-5_5.

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Oakland, John, e Robert Oakland. "Other types of control charts for variables". In Statistical Process Control, 149–87. 7^a ed. 7th edition. | Abingdon, Oxon ; New York, NY : Routledge, 2019.: Routledge, 2018. http://dx.doi.org/10.4324/9781315160511-7.

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Wetherill, G. Barrie, e Don W. Brown. "The design of control charts from specification limits". In Statistical Process Control, 192–211. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-2949-5_9.

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Wetherill, G. Barrie, e Don W. Brown. "Further theoretical results on control charts for continuous variables". In Statistical Process Control, 170–91. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-2949-5_8.

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Wetherill, G. Barrie, e Don W. Brown. "Extensions to Shewhart charts for one-at-a-time data". In Statistical Process Control, 114–37. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-2949-5_6.

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Reynolds, Marion R., e Jianying Lou. "A GLR Control Chart for Monitoring the Process Variance". In Frontiers in Statistical Quality Control 10, 3–17. Heidelberg: Physica-Verlag HD, 2012. http://dx.doi.org/10.1007/978-3-7908-2846-7_1.

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Stolpa, James W. "Statistical Process Control and Control Charts". In 60th Porcelain Enamel Institute Technical forum: Ceramic Engineering and Science Proceedings, Volume 19, Issue 5, 25–27. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2008. http://dx.doi.org/10.1002/9780470294512.ch7.

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Yang, Su-Fen, e Shan-Wen Lu. "An Average Loss Control Chart Under a Skewed Process Distribution". In Frontiers in Statistical Quality Control 13, 65–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67856-2_5.

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Kawamura, Hironobu, Ken Nishina e Tomomichi Suzuki. "Process Adjustment Control Chart for Simultaneous Monitoring of Process Capability and State of Statistical Control". In Frontiers in Statistical Quality Control 10, 121–30. Heidelberg: Physica-Verlag HD, 2012. http://dx.doi.org/10.1007/978-3-7908-2846-7_9.

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Atti di convegni sul tema "Statistical Process control chart":

Bringhurst, Katlynn, Joel Walter e Scott Best. "CAATS – Automotive Wind Tunnel Statistical Process Control". In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2542.

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<div class="section abstract"><div class="htmlview paragraph">This paper presents the application of statistical process control (SPC) methods to Windshear, a 180-mph motorsports and automotive wind tunnel equipped with a wide-belt rolling road system. The SPC approach captures the complete variability of the facility and offers useful process performance metrics that are based on a sound statistical framework. Traditional control charts are explored, emphasizing the uniqueness of variability experienced in wind tunnels which includes significant, unexplained short-term and long-term variation compared to typical manufacturing processes. This unique variation is elegantly captured by the three-way control chart, which is applied to estimate the complete process reproducibility with different levels of repeatability of vehicle drag coefficient. The sensitivity of three-way control charts is explored including the evaluation of an alternate group assignment within the same dataset. A practical example is provided evaluating secondary boundary layer fan performance after a failure event to demonstrate the effectiveness of three-way control charts in establishing and maintaining a rigorous automotive wind tunnel maintenance program. SPC charts and their resultant analysis are a function of the test matrix that generates them. The development of a rigorous SPC test matrix is discussed including an example provided by Windshear. This paper is part of a global effort to Commonize Automotive Aerodynamic Testing Standards (CAATS) launched by the Subsonic Aerodynamic Testing Association (SATA).</div></div>

Khan, Mansoor S., e Lirong Cui. "Statistical process control based chart for information systems security". In Seventh International Conference on Digital Image Processing (ICDIP15), a cura di Charles M. Falco e Xudong Jiang. SPIE, 2015. http://dx.doi.org/10.1117/12.2197092.

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Fong, Sze Jeeu, Sze San Nah e Pooi Ah Hin. "New control chart for multivariate process". In 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE2012). IEEE, 2012. http://dx.doi.org/10.1109/icssbe.2012.6396609.

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Huat, Ng Kooi, e Habshah Midi. "Robust control chart for change point detection of process variance in the presence of disturbances". In THE 2ND ISM INTERNATIONAL STATISTICAL CONFERENCE 2014 (ISM-II): Empowering the Applications of Statistical and Mathematical Sciences. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4907463.

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Xu, Yafei, e Ostap Okhrin. "A Nonparametric Multivariate Statistical Process Control Chart Based on Change Point Model". In Annual Meeting of the International Society for Data Science and Analytics. ISDSA Press, 2020. http://dx.doi.org/10.35566/isdsa2019c2.

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Amayri, Manar, Stephane Ploix, Fatma Najar, Nizar Bouguila e Frederic Wurtz. "A Statistical Process Control Chart Approach for Occupancy Estimation in Smart Buildings". In 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2019. http://dx.doi.org/10.1109/ssci44817.2019.9002873.

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Hayati, F., S. Maghsoodloo e C. Lemiere. "98. Statistical Process Control Chart for Monitoring of Lung Function in Occupational Asthma". In AIHce 2004. AIHA, 2004. http://dx.doi.org/10.3320/1.2758313.

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Rickli, Jeremy, e Jaime Camelio. "Monitoring and Diagnosis of Assembly Fixture Faults Using Modified Multivariate Control Charts and Surface Scanning Content". In ASME 2007 International Manufacturing Science and Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/msec2007-31075.

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Recent advances in process monitoring technology have introduced an influx of exceptionally large data sets containing information on manufacturing process health. Recorded data sets are comprised of numerous parameters for which multivariate statistical process control (MSPC) methodologies are required. Current multivariate control charts are ideal for monitoring data sets with a minimal amount of parameters, however, new monitoring devices such as surface scanning cameras increase the number of parameters by two orders of magnitude in some cases. This paper proposes a modified form of the original multivariate Hotelling T2 chart possessing the capability to monitor manufacturing processes containing a large number of parameters and a fault diagnosis procedure incorporating least squares analysis in conjunction with univariate control charts. A case study considering surface scanning of compliant sheet metal components and comparisons to processes utilizing Optical CMM’s is presented as verification of the proposed assembly fixture fault diagnosis methodology and modified Hotelling T2 multivariate control chart.

Wang, Haiyan. "A New Method for Feature Selection in Pattern Recognition of Statistical Process Control Chart". In 2022 China Automation Congress (CAC). IEEE, 2022. http://dx.doi.org/10.1109/cac57257.2022.10056087.

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Heryanto, Andika Yuli. "Bakpia Quality Control Analysis Using the Statistical Quality Control (SQC) Method (Case Study: SMEs Bakpia Latief, Kediri<i>)</i>". In The 6th International Conference on Science and Engineering. Switzerland: Trans Tech Publications Ltd, 2024. http://dx.doi.org/10.4028/p-fzb5j6.

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Bakpia is made from green beans and sugar wrapped in flour. Bakpia Latief Kediri is a bakpia industry in the City of Kediri. The company needs help in the production process activities. There are still products that need to be fixed. The types of it defects in the company are burnt and cracked. This study aimed to determine the performance of its quality control, determine the most dominant types of defects in its products, and provide suggestions for improving its production process. The research method uses the Statistical Quality Control method. Data is taken in the form of primary data and secondary data. Primary data was collected by interview and observation. Interviews were conducted using in-depth interviews. Observations were made for 30 days. Data analysis uses statistical tools, which include check sheets, histograms, fishbone diagrams, Pareto diagrams, and control charts p. Bakpia quality control at Bakpia Latief is under control because the p chart control chart shows that only one point is out of the control limits. The most dominant defect in its products is burnt, with an average percentage of 0.617%. Corrective actions to reduce the existence of defective products, namely repairing and maintaining production machines, improving the stages of the production process, developing human resources, and repairing production facilities. Keywords: Product Defects, Quality Control, Statistical Quality Control

Rapporti di organizzazioni sul tema "Statistical Process control chart":

Pulsipher, B. A., e W. L. Kuhn. Statistical process control applied to the liquid-fed ceramic melter process. Office of Scientific and Technical Information (OSTI), settembre 1987. http://dx.doi.org/10.2172/5988542.

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Krajcsik, Stephen. The Use of Statistical Methods in Dimensional Process Control. Fort Belvoir, VA: Defense Technical Information Center, settembre 1985. http://dx.doi.org/10.21236/ada444590.

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Dessouky, M. I., R. E. DeVor e S. S. Kapoor. Statistical Process Control for Evaluating Contract Service at Army Installations. Fort Belvoir, VA: Defense Technical Information Center, settembre 1990. http://dx.doi.org/10.21236/ada228405.

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CROWDER, STEPHEN V. Small Sample Properties of an Adaptive Filter with Application to Low Volume Statistical Process Control. Office of Scientific and Technical Information (OSTI), settembre 1999. http://dx.doi.org/10.2172/12655.

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Tarko, Andrew P., Mario Romero, Cristhian Lizarazo e Paul Pineda. Statistical Analysis of Safety Improvements and Integration into Project Design Process. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317121.

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RoadHAT is a tool developed by the Center for Road Safety and implemented for the INDOT safety management practice to help identify both safety needs and relevant road improvements. This study has modified the tool to facilitate a quick and convenient comparison of various design alternatives in the preliminary design stage for scoping small and medium safety-improvement projects. The modified RoadHAT 4D incorporates a statistical estimation of the Crash Reduction Factors based on a before-and-after analysis of multiple treated and control sites with EB correction for the regression-to-mean effect. The new version also includes the updated Safety Performance Functions, revised average costs of crashes, and the comprehensive table of Crash Modification Factors—all updated to reflect current Indiana conditions. The documentation includes updated Guidelines for Roadway Safety Improvements. The improved tool will be implemented at a sequence of workshops for the final end users and preceded with a beta-testing phase involving a small group of INDOT engineers.

Willson. L51756 State of the Art Intelligent Control for Large Engines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), settembre 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other tasks, however, computers perform very poorly at tasks that humans find to be trivial. A discussion of the differences in the way humans and computer process information is crucial to an understanding of the field of artificial intelligence. In this project, several artificial intelligence/ intelligent control systems were examined: heuristic search techniques, adaptive control, expert systems, fuzzy logic, neural networks, and genetic algorithms. Of these, neural networks showed the most potential for use on large bore engines because of their ability to recognize patterns in incomplete, noisy data. Two sets of experimental tests were conducted to test the predictive capabilities of neural networks. The first involved predicting the ignition timing from combustion pressure histories; the best networks responded within a specified tolerance level 90% to 98.8% of the time. In the second experiment, neural networks were used to predict NOx, A/F ratio, and fuel consumption. NOx prediction accuracy was 91.4%, A/F ratio accuracy was 82.9%, and fuel consumption accuracy was 52.9%. This report documents the assessment of the state of the art of artificial intelligence for application to the monitoring and control of large-bore natural gas engines.

Khvostina, Inesa, Serhiy Semerikov, Oleh Yatsiuk, Nadiia Daliak, Olha Romanko e Ekaterina Shmeltser. Casual analysis of financial and operational risks of oil and gas companies in condition of emergent economy. [б. в.], ottobre 2020. http://dx.doi.org/10.31812/123456789/4120.

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The need to control the risk that accompanies businesses in their day- to-day operations, and at the same time changing economic conditions make risk management an almost indispensable element of economic life. Selection of the main aspects of the selected phases of the risk management process: risk identification and risk assessment are related to their direct relationship with the subject matter (risk identification to be managed; risk analysis leading to the establishment of a risk hierarchy, and, consequently, the definition of risk control’ methods) and its purpose (bringing the risk to acceptable level). It is impossible to identify the basic patterns of development of the oil and gas industry without exploring the relationship between economic processes and enterprise risks. The latter are subject to simulation, and based on models it is possible to determine with certain probability whether there have been qualitative and quantitative changes in the processes, in their mutual influence on each other, etc. The work is devoted to exploring the possibilities of applying the Granger test to examine the causal relationship between the risks and obligations of oil and gas companies. The analysis is based on statistical tests and the use of linear regression models.

Rans. PR-352-11703-R01 Maintenance and Test Intervals for Primary and Secondary Measurement Equipment. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), marzo 2013. http://dx.doi.org/10.55274/r0010801.

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This report summarizes the work done to investigate existing maintenance and test intervals for primary and secondary measurement equipment. Advances in the accuracy and reliability of current measurement technology and the ability to apply Statistical Process Control (SPC) techniques to identify abnormal operating conditions that need to be investigated is changing the way we need to look at maintenance. Test intervals are determined based on four factors which are discussed in more detail in Section 3.4.2 Determination of Test Intervals and Tolerance Bands. Ignore Routine Variability � e.g.: Don�t continually zero adjust an instrument which is experiencing small changes due to normal operating condition changes such as ambient temperature. Determine a Test Frequency Based on Equipment Degradation with Time � Use equipment reliability information to determine if the equipment routinely drifts. Determine a Test Frequency Based on How Long an Exception/Unexpected Meter Problem Can be Tolerated � The frequency is often related to the financial impacts and the size of the facility. Determine a Test Frequency Based on Contractual or Commercial Requirements � For example if the maximum period a measurement correction can be applied is 6 months, a test frequency of 6 to 12 months may be required to meet the requirement. The primary and secondary test interval is determined from the shortest test frequency determined by following these four steps.

Gustafsson, Marcus, e Stephanie Cordova. Värdeskapande av koldioxid från biogasproduktion. Linköping University Electronic Press, settembre 2023. http://dx.doi.org/10.3384/9789180753838.

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arbon dioxide (CO₂) has a negative impact on the climate, but it also has several practical areas of use. Many industrial processes emit CO₂ in high concentrations, which could be captured to mitigate emissions while also creating valuable products. One example of such a process is biogas upgrading – a process separating renewable gases, where methane is taken care of for use as vehicle fuel or industrial energy carrier, while CO₂ is released into the atmosphere. The aim of this project has been to chart alternatives and technologies for taking care of green CO₂ from biogas upgrading, so-called carbon capture and utilization (CCU), and to investigate the conditions for applying these in a Swedish context. The work has been guided by the following research questions: * How large is the current and future potential for CCU from biogas production? * What are the possible areas of use for CO₂ from biogas production? * What factors influence the choice of areas of use for CO₂ from biogas production? * How large is the environmental benefit of CCU from biogas production? To answer these questions, calculations of potentials, a multi-criteria assessment and a life cycle assessment were carried out, based on the Swedish biogas production. A reference group comprising representatives for large Swedish companies within biogas production and biogas upgrading technology was used to enable coproduction and networking between the research group and the business sector. The production of CO₂ from biogas was estimated to 160,000 ton/year in 2020, with potential to increase to 540,000 – 840,000 ton/year in a few years and 790,000 – 1,230,000 ton/year in a longer perspective, as a consequence of an expected increase in the Swedish biogas production. A large share of the CO₂ is however produced at relatively small upgrading facilities, which could limit the feasibility for CCU due to high costs for investment and operation. Adding hydrogen to transform all the CO₂ into methane could potentially increase the methane production from biogas from 2 to 3 TWh/year in a short-term perspective and from 11 to 17 TWh/year in a long-term perspective, given sufficient access to hydrogen. Other ways of utilizing CO₂ from biogas include production of biomass or chemicals, concrete curing, pH control of process water and use as a refrigerant. The choice of CCU options can be influenced by environmental, technical, economic and policy-related aspects. From the biogas producers’ perspective, methanation is the option that is the most compatible with the existing production system and business model, while other solutions usually involve another actor taking care of the CO₂. Hydrogen is required for methanation as well as for production of chemicals. Another limiting factor are the high purity requirements on all CO₂ that is distributed and sold on the market. The geographical distribution of the production plants can also be a challenge. Several CCU options can improve the environmental performance of biogas by replacing fossil-based products. The potential climate impact is the lowest if the CO₂ is methanized with renewable hydrogen or mineralized in concrete, but other forms of environmental impact can also be reduced by applying these or other CCU options. For comparison, permanent storage of CO₂ in geological formations (carbon capture and storage, CCS) only reduces the climate impact, while it increases other forms of environmental impact. Furthermore, permanently storing biogenic CO₂ can make it difficult to reduce the use of fossil CO₂ and transition to a more sustainable society. The need for carbon in many essential processes and products suggests that biogenic CO₂ should be utilized and not stored.

Treadwell, Jonathan R., James T. Reston, Benjamin Rouse, Joann Fontanarosa, Neha Patel e Nikhil K. Mull. Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes. Agency for Healthcare Research and Quality (AHRQ), marzo 2021. http://dx.doi.org/10.23970/ahrqepctb38.

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Background. Automated-entry consumer devices that collect and transmit patient-generated health data (PGHD) are being evaluated as potential tools to aid in the management of chronic diseases. The need exists to evaluate the evidence regarding consumer PGHD technologies, particularly for devices that have not gone through Food and Drug Administration evaluation. Purpose. To summarize the research related to automated-entry consumer health technologies that provide PGHD for the prevention or management of 11 chronic diseases. Methods. The project scope was determined through discussions with Key Informants. We searched MEDLINE and EMBASE (via EMBASE.com), In-Process MEDLINE and PubMed unique content (via PubMed.gov), and the Cochrane Database of Systematic Reviews for systematic reviews or controlled trials. We also searched ClinicalTrials.gov for ongoing studies. We assessed risk of bias and extracted data on health outcomes, surrogate outcomes, usability, sustainability, cost-effectiveness outcomes (quantifying the tradeoffs between health effects and cost), process outcomes, and other characteristics related to PGHD technologies. For isolated effects on health outcomes, we classified the results in one of four categories: (1) likely no effect, (2) unclear, (3) possible positive effect, or (4) likely positive effect. When we categorized the data as “unclear” based solely on health outcomes, we then examined and classified surrogate outcomes for that particular clinical condition. Findings. We identified 114 unique studies that met inclusion criteria. The largest number of studies addressed patients with hypertension (51 studies) and obesity (43 studies). Eighty-four trials used a single PGHD device, 23 used 2 PGHD devices, and the other 7 used 3 or more PGHD devices. Pedometers, blood pressure (BP) monitors, and scales were commonly used in the same studies. Overall, we found a “possible positive effect” of PGHD interventions on health outcomes for coronary artery disease, heart failure, and asthma. For obesity, we rated the health outcomes as unclear, and the surrogate outcomes (body mass index/weight) as likely no effect. For hypertension, we rated the health outcomes as unclear, and the surrogate outcomes (systolic BP/diastolic BP) as possible positive effect. For cardiac arrhythmias or conduction abnormalities we rated the health outcomes as unclear and the surrogate outcome (time to arrhythmia detection) as likely positive effect. The findings were “unclear” regarding PGHD interventions for diabetes prevention, sleep apnea, stroke, Parkinson’s disease, and chronic obstructive pulmonary disease. Most studies did not report harms related to PGHD interventions; the relatively few harms reported were minor and transient, with event rates usually comparable to harms in the control groups. Few studies reported cost-effectiveness analyses, and only for PGHD interventions for hypertension, coronary artery disease, and chronic obstructive pulmonary disease; the findings were variable across different chronic conditions and devices. Patient adherence to PGHD interventions was highly variable across studies, but patient acceptance/satisfaction and usability was generally fair to good. However, device engineers independently evaluated consumer wearable and handheld BP monitors and considered the user experience to be poor, while their assessment of smartphone-based electrocardiogram monitors found the user experience to be good. Student volunteers involved in device usability testing of the Weight Watchers Online app found it well-designed and relatively easy to use. Implications. Multiple randomized controlled trials (RCTs) have evaluated some PGHD technologies (e.g., pedometers, scales, BP monitors), particularly for obesity and hypertension, but health outcomes were generally underreported. We found evidence suggesting a possible positive effect of PGHD interventions on health outcomes for four chronic conditions. Lack of reporting of health outcomes and insufficient statistical power to assess these outcomes were the main reasons for “unclear” ratings. The majority of studies on PGHD technologies still focus on non-health-related outcomes. Future RCTs should focus on measurement of health outcomes. Furthermore, future RCTs should be designed to isolate the effect of the PGHD intervention from other components in a multicomponent intervention.