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Journal articles on the topic 'Statistical quality control methods'

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Published: 28 May 2022

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1

Nagata, Yasushi. "Statistical Methods for Quality Control." Seikei-Kakou 31, no. 4 (March 20, 2019): 132–36. http://dx.doi.org/10.4325/seikeikakou.31.132.

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2

Зенкін, Микола Анатолійович. "Print Quality Control Using Statistical Methods." Технологія і техніка друкарства, no. 3(69) (November 10, 2020): 52–58. http://dx.doi.org/10.20535/2077-7264.3(69).2020.217390.

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Розглянуто можливість використання статистичних методів для забезпечення виробництва продукції, яка відповідає вимогам споживачів з найменшими витратами. Виконано аналіз різних досліджень, що дозволило виявити споживчі вимоги до кінцевої продукції. Описано принципи та практичні методи Загального менеджменту якості TQM. Відмічено, що суворий контроль, який покликаний забезпечити максимальну для друкованого виробництва якість, не обмежений одним калібрувальним тестовим тиражом — результати друку необхідно перевіряти постійно. Встановлено, що у стандарті ISО 12647-2:2004 відсутні: оптимальні значення густини для трьох типів паперу; значення CIELАB для балансу по сірому; параметри паперу конкретних виробників; параметри кольорів конкретних виробників; параметри пластин конкретних виробників; специфічні добавки та інші допоміжні матеріали для друку. Стандарт визначає вимірювані результати, на які потрібно вийти, але не дає методики та рекомендацій з їх досягнення. Запропоновано можливість застосування методу побудови контрольної карти для виявлення причин відхилення показників оптичної густини для тріади фарб в офсетному друці. З’ясовано, що найбільш важливими вимогами споживачів є розрізнення дрібних деталей зображення, тексту, чіткість друку, відсутність плям і сторонніх елементів на зображенні, естетичність. Розроблені в роботі підходи дозволяють визначити найбільш слабке місце в системі папір (картон)—друкарська фарба і оцінити рівень якості.
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3

Prístavka, Miroslav, and Marián Bujna. "Use of Statistical Methods in Quality Control." Acta Technologica Agriculturae 16, no. 2 (June 1, 2013): 35–38. http://dx.doi.org/10.2478/ata-2013-0009.

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Abstract More and more organisations are beginning to realise how important is it to implement a quality management system. Its main task is to rejuvenate the given processes in production. Problem solving is systemic within quality assurance procedures of an organisation. This work describes a quality management system according to ISO 9001 and statistical methods in quality management. The theoretical part contains the characteristics and description of the system listed above. The practical part shows the use of knowledge in the organisation to solve problems.
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4

Nembhard, Harriet Black. "Statistical Process Adjustment Methods for Quality Control." Journal of the American Statistical Association 99, no. 466 (June 2004): 567–68. http://dx.doi.org/10.1198/jasa.2004.s340.

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5

Yankovich, E., O. Nevidimova, and K. Yankovich. "Methods of Statistical Control for Groundwater Quality Indicators." IOP Conference Series: Materials Science and Engineering 132 (June 2016): 012019. http://dx.doi.org/10.1088/1757-899x/132/1/012019.

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6

Paksy, L. "Use of mathematical-statistical methods in spectrochemical quality control." Microchemical Journal 45, no. 3 (June 1992): 318–28. http://dx.doi.org/10.1016/0026-265x(92)90091-g.

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7

van Delft, Christian. "Some New Classroom Cases for Teaching Statistical Quality Control Methods." Quality Engineering 14, no. 1 (January 2002): 45–48. http://dx.doi.org/10.1081/qen-100106885.

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8

Stanley, James D. "Statistical Methods for Industrial Process Control." Journal of Quality Technology 30, no. 3 (July 1998): 303–5. http://dx.doi.org/10.1080/00224065.1998.11979862.

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9

Stepanov, A. M., and N. V. Khashimova. "USE OF STATISTICAL QUALITY CONTROL METHODS IN MANUFACTURING BRIDGE METAL STRUCTURES." Kontrol'. Diagnostika, no. 253 (July 2019): 30–37. http://dx.doi.org/10.14489/td.2019.07.pp.030-037.

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This article describes the use of statistical methods of quality control and management in the manufacturing metal bridge structures, such as: – cause and effect diagram (Ishikawa diagram). Causal factors affecting the quality of welded joints were established; – checklist. The form of the control sheet for registration of types of surface defects of butt welded joints were developed; – pareto Chart. The most common types of surface defects of butt welded joints were identified; the types of surface defects of butt welded joints, leading to the greatest losses were determined. ABC method – analysis of Pareto diagram of internal defects of welded joints. General conclusions on the results of their application were made.
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10

Simonenko, K. V., G. S. Bulatova, L. B. Antropova, and L. G. Varepo. "Quality Control of the Print with the Application of Statistical Methods." Journal of Physics: Conference Series 998 (April 2018): 012032. http://dx.doi.org/10.1088/1742-6596/998/1/012032.

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11

Eskov, D., D. Tsibaev, and V. Eskova. "Statistical methods for quality control sowing seeds on the forest clearing." Актуальные направления научных исследований XXI века: теория и практика 3, no. 2 (May 1, 2015): 38–42. http://dx.doi.org/10.12737/11025.

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12

Kazantseva, L. L., S. E. Artemenko, T. P. Ustinova, and V. N. Marinchev. "Use of statistical control methods for assessing the quality of polycaproamide." Fibre Chemistry 32, no. 6 (November 2000): 461–67. http://dx.doi.org/10.1007/bf02359083.

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13

Golińska, Ewa, and Marcin Zemczak. "Statistical Methods in the Automotive Industry." Applied Mechanics and Materials 791 (September 2015): 56–62. http://dx.doi.org/10.4028/www.scientific.net/amm.791.56.

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Controlling of the manufacturing processes is one of the key factors that allow companies to produce goods of high quality. In the article one of methods of the quality management - statistical process control (SPC) has been discussed. On the example of the car headrests manufacturing enterprise benefits from applying this method have been presented. Authors pointed out that the use of statistical methods in decision-making processes must not be difficult and time-consuming, if is being supported by a computer with using inexpensive tools.
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14

Ilstrup, D. M. "Statistical methods in microbiology." Clinical Microbiology Reviews 3, no. 3 (July 1990): 219–26. http://dx.doi.org/10.1128/cmr.3.3.219.

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Statistical methodology is viewed by the average laboratory scientist, or physician, sometimes with fear and trepidation, occasionally with loathing, and seldom with fondness. Statistics may never be loved by the medical community, but it does not have to be hated by them. It is true that statistical science is sometimes highly mathematical, always philosophical, and occasionally obtuse, but for the majority of medical studies it can be made palatable. The goal of this article has been to outline a finite set of methods of analysis that investigators should choose based on the nature of the variable being studied and the design of the experiment. The reader is encouraged to seek the advice of a professional statistician when there is any doubt about the appropriate method of analysis. A statistician can also help the investigator with problems that have nothing to do with statistical tests, such as quality control, choice of response variable and comparison groups, randomization, and blinding of assessment of response variables.
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15

Eygü, Hakan, and M. Suphi Özçomak. "Multivariate Statistical Quality Control Based on Ranked Set Sampling." Asian Social Science 14, no. 1 (December 14, 2017): 1. http://dx.doi.org/10.5539/ass.v14n1p1.

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The sample of the study was formed using simple random sampling, ranked set sampling, extreme ranked set sampling and median ranked set sampling. At the end of this process, the researcher created Hotelling’s T2 control charts, a multivariate statistical process control method. The performances of SRS, RSS, ERSS and MRSS sampling methods were compared to one another using these control charts. A simulation was performed to see the average run-length values for Hotelling’s T2 control charts, and these findings were also used for the comparison of the sampling performances.At the end of the study, the researcher formed a sample using median ranked set sampling and created the Hotelling’s T2 control chart. As a result of this operation, the researcher found that there was an out-of-control signal in the process, while there was no such signal in other sampling methods. When the average run-length values obtained from Hotelling’s T2 control charts were compared, it was seen that a shift in the process was detected by the ranked set sampling earlier, when compared to other sampling methods. This paper it can be said that the methods used are unique to the literature because they are applied to multivariate data.
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16

Benneyan, James C. "Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part II: Chart Use, Statistical Properties, and Research Issues." Infection Control and Hospital Epidemiology 19, no. 4 (April 1998): 265–83. http://dx.doi.org/10.2307/30142419.

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17

Birnbaum, David, and James C. Benneyan. "Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part II: Chart Use, Statistical Properties, and Research Issues." Infection Control & Hospital Epidemiology 19, no. 4 (April 1998): 265–83. http://dx.doi.org/10.1017/s0195941700087397.

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ABSTRACTThis is the second in a two-part series discussing and illustrating the application of statistical process control (SPC) in hospital epidemiology. The basic philosophical and theoretical foundations of statistical quality control and their relation to epidemiology are emphasized in order to expand the mutual understanding and cross-fertilization between these two disciplines. Part I provided an overview of the philosophy and general approach of SPC, illustrated common types of control charts, and provided references for further information or statistical formulae. Part II now discusses alternate possible SPC approaches, statistical properties of control charts, chart-design issues and optimal control limit widths, some common misunderstandings, and more advanced issues. The focus of both articles is mostly nonmathematical, emphasizing important concepts and practical examples rather than academic theory and exhaustive calculations.
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18

Benneyan, James C. "Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part II: Chart Use, Statistical Properties, and Research Issues." Infection Control and Hospital Epidemiology 19, no. 4 (April 1998): 265–83. http://dx.doi.org/10.1086/647807.

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19

Medvedev, Rostislav, and Yuliya Bakhracheva. "Application of Statistical Quality Control Methods for Optimizing the Track Repair Production." Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 10. Innovatcionnaia deiatel’nost’, no. 3 (October 20, 2014): 93–97. http://dx.doi.org/10.15688/jvolsu10.2014.3.11.

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20

Kbiladze, David, and Shorena Metreveli. "THE SEARCH FOR MODERN METHODS FOR THE STATISTICAL QUALITY CONTROL OF SERVICES." Ekonomika 96, no. 2 (November 2, 2017): 102–9. http://dx.doi.org/10.15388/ekon.2017.2.11003.

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The countries with developed market economies have vast experience in using the statistical methods in running business. In this respect, the use of Six Sigma techniques and tools to control the output (service) quality is worthwhile. This technique, if introduced in Georgia, can be much beneficial for the businessmen. Different-profile companies use different statistical methods for the output (service) quality control. However, it is the Six Sigma techniques that yield the most pragmatic outputs. This tool enables the different-tier managers to rely not only on their intuition in making managerial decisions but also on the quantitative data obtained through the introduction of 6σ to their companies. The measures to search for the modern methods of introduction and better use of the 6σ concept seek to improve the output (production) quality of different sites of a corporation, while also meeting the changing demands of customer markets and shareholders. In the search of such measures, the authors of the present article attempted to examine the effect of the 6σ set beyond the limits of a corporation and, by considering the public opinion about the corporation, conduct the statistical control of the output (service) offered by the corporation to its clients. The pilot study, accomplished with this purpose by the authors of the article, together with their students, gave quite interesting results.
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21

Lu, Jye-Chyi, Shuen-Lin Jeng, and Kaibo Wang. "A Review of Statistical Methods for Quality Improvement and Control in Nanotechnology." Journal of Quality Technology 41, no. 2 (April 2009): 148–64. http://dx.doi.org/10.1080/00224065.2009.11917770.

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22

Makarov, R. I., V. I. Mazanova, and Yu M. Obukhov. "The process approach and statistical methods in quality control of hardened glass." Glass and Ceramics 65, no. 9-10 (September 2008): 328–30. http://dx.doi.org/10.1007/s10717-009-9069-y.

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23

Temasova, G. N., O. A. Leonov, N. Zh Shkaruba, Yu G. Vergazova, and D. I. Petrovskiy. "Application of statistical methods for quality control of agro-industrial production processes." IOP Conference Series: Earth and Environmental Science 839, no. 2 (September 1, 2021): 022026. http://dx.doi.org/10.1088/1755-1315/839/2/022026.

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Abstract The article deals with the mechanism of quality control of agro-industrial production processes, namely, quality control of poultry farm processes. It also details the management of nonconforming products using simple quality control and management tools – a checklist and a Pareto chart. It was suggested that a checklist should be used to record nonconformities and their number for each nonconformity. Using this checklist form will allow you to analyze the production process, identify the causes of nonconformities, and quickly intervene in the process to prevent nonconformities from occurring. To analyze the inconsistencies and the causes of their occurrence, it was suggested to use the Pareto diagram, the use of which allows you to pay attention to the most significant problems.
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24

Montgomery, Douglas C., and Christina M. Mastrangelo. "Some Statistical Process Control Methods for Autocorrelated Data." Journal of Quality Technology 23, no. 3 (July 1991): 179–93. http://dx.doi.org/10.1080/00224065.1991.11979321.

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25

Zhuravlev, G. M., A. E. Gvozdev, S. V. Sapozhnikov, S. N. Kutepov, and E. V. Ageev. "DECISIONS ON STATISTICAL MODELS IN QUALITY CONTROL OF PRODUCTS." Proceedings of the Southwest State University 21, no. 5 (October 28, 2017): 78–92. http://dx.doi.org/10.21869/2223-1560-2017-21-5-78-92.

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Development of methods for registration, description and analysis of statistical experimental data, obtained by monitoring mass random phenomena is the subject of a special science - mathematical statistics. All tasks of mathematical statistics concerns the treatment of observations of mass random phenomena, but depending on the nature of the solved practical question and amount of available experimental material these tasks can take a particular form. One of the main objectives of mathematical statistics is to develop methods of studying mass phenomena or processes on the basis of the relatively small number of observations or experiments. These methods have their scientific justification, his theory, called the theory of samples. The aim of this work is to build mathematical models of influence of various factors on a single number using the method of multifactor experiment planning, and their use results in the appointment of modes of technological operations. To study processes incomplete hot deformation uses a complex viscoplastic model of the environment, the mechanical properties which are characterized by a yield stress and viscosity. The yield strength depends on temperature and strain rate. On this basis, was carried out processing of experimental data by the method of multifactor experiment planning and statistical treatment of experimental data by definition of the yield strength depending on temperature and speed of deformation of steel U12A. From the analysis of the obtained regression equations, we can conclude that the most highly specific force depends on temperature. Regression equations mathematically describe the mutual influence of technological factors on yield strength and specific strength, in addition they allow you to correctly set processing modes that yield products of the required quality.
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Wang, Wenbin, and Wenjuan Zhang. "Early defect identification: application of statistical process control methods." Journal of Quality in Maintenance Engineering 14, no. 3 (August 15, 2008): 225–36. http://dx.doi.org/10.1108/13552510810899445.

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27

Prístavka, Miroslav, Martina Kotorová, and Radovan Savov. "Quality Control in Production Processes." Acta Technologica Agriculturae 19, no. 3 (September 1, 2016): 77–83. http://dx.doi.org/10.1515/ata-2016-0016.

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AbstractThe tools for quality management are used for quality improvement throughout the whole Europe and developed countries. Simple statistics are considered one of the most basic methods. The goal was to apply the simple statistical methods to practice and to solve problems by using them. Selected methods are used for processing the list of internal discrepancies within the organization, and for identification of the root cause of the problem and its appropriate solution. Seven basic quality tools are simple graphical tools, but very effective in solving problems related to quality. They are called essential because they are suitable for people with at least basic knowledge in statistics; therefore, they can be used to solve the vast majority of problems.
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28

Garrison, Eric, and Joshua New. "Quality Control Methods for Advanced Metering Infrastructure Data." Smart Cities 4, no. 1 (January 28, 2021): 195–203. http://dx.doi.org/10.3390/smartcities4010012.

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While urban-scale building energy modeling is becoming increasingly common, it currently lacks standards, guidelines, or empirical validation against measured data. Empirical validation necessary to enable best practices is becoming increasingly tractable. The growing prevalence of advanced metering infrastructure has led to significant data regarding the energy consumption within individual buildings, but is something utilities and countries are still struggling to analyze and use wisely. In partnership with the Electric Power Board of Chattanooga, Tennessee, a crude OpenStudio/EnergyPlus model of over 178,000 buildings has been created and used to compare simulated energy against actual, 15-min, whole-building electrical consumption of each building. In this study, classifying building type is treated as a use case for quantifying performance associated with smart meter data. This article attempts to provide guidance for working with advanced metering infrastructure for buildings related to: quality control, pathological data classifications, statistical metrics on performance, a methodology for classifying building types, and assess accuracy. Advanced metering infrastructure was used to collect whole-building electricity consumption for 178,333 buildings, define equations for common data issues (missing values, zeros, and spiking), propose a new method for assigning building type, and empirically validate gaps between real buildings and existing prototypes using industry-standard accuracy metrics.
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29

Kleymenova, N. L., L. I. Nazina, I. N. Bolgova, A. N. Pegina, and O. A. Orlovseva. "Quality control in the production process of sunflower oil." IOP Conference Series: Earth and Environmental Science 845, no. 1 (November 1, 2021): 012111. http://dx.doi.org/10.1088/1755-1315/845/1/012111.

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Abstract The typical problem of vegetable oil processing is to ensure the consistency of the output quality. The one parameter that mostly affects quality is the presence of wax, which commands control at all stages of the process (refinement, odours removal, freezing). Statistical methods of analysis can be usefully applied to the improvement of vegetable oil processing, as demonstrated by this study. The authors in fact used statistical methods in order to: a) optimize parameters consistency, b) enhance process efficiency, c) improve economic performance and finally, d) assess process stability. The following statistical tools were used in the study: 1) Histograms, 2) Shewhart Charts, 3) Ishikawa Diagrams and, 4) Pareto Chart. A first major finding was that the occurrence of process flaws that would result in product rejection had a 5% probability of happening at all stages of the process. Moreover, the analysis of process stability with maps of average values and ranges leads to the finding that the process itself is statistically unstable. Finally, cause-and-effect relationships of influencing factors (such as the quality of feedstock) were investigated, thus determining the main causes of flaw in the production process. This leads to the definition of corrective actions, the effectiveness of which was then investigated and evaluated.
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Kubalcik, Marek, Vladimir Bobal, and Tomas Barot. "Statistical analysis of control quality of MPC using testing hypothesis." MATEC Web of Conferences 292 (2019): 01037. http://dx.doi.org/10.1051/matecconf/201929201037.

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Methods of the statistical induction have a significant role in the quantitative research. In a wide spectrum of research areas, the methods based on testing hypotheses have been frequently used. However, in the area of the process control, testing hypothesis has not been widely considered as an established tool for signal analyses, although signals in control loops are suitable for analysis by means of quantitative statistical methods due to their stochastic character. Particularly, a statistical paired comparison can be applied for analysis of control quality achieved with different control algorithms. This comparison can be based on a paired comparison of corresponding signals obtained with different or modified control algorithms. The aim of this paper is a proposal of incorporation of testing hypothesis to analysis of control quality. The analysis was performed on a strictly defined significance level 0.001, which is a standardly used value in technical applications. As an example was demonstrated analysis of control quality achieved with two versions of a predictive controller. Finally, achieved results of paired comparison using testing hypothesis are discussed.
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31

Thomann, M. "Quality evaluation methods for wastewater treatment plant data." Water Science and Technology 57, no. 10 (May 1, 2008): 1601–9. http://dx.doi.org/10.2166/wst.2008.151.

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Non identified systematic errors in data sets can cause severe problems inducing wrong decisions in function control, process modelling or planning of new treatment infrastructure. In this paper statistical methods are shown to identify systematic errors in full-scale WWTP data sets. With a redundant mass balance approach analyzing five different mass balances, systematic errors of about 10%–20% compared to the input fluxes can be identified at a 5%-significance level. A Shewhart control-chart approach to survey the data quality of on-line-sensors allows a statistical as well as a fast graphical analysis of the measurement process. A 19 month data set indicates that NO3−, PO4− and NH4− on-line analyzers in the filter effluent and MLSS sensors in the aeration tanks were not disturbed by any systematic error for 85–95% of the measuring time. The in-control-interval (±3·standard deviation) has a width of ±12–17% (NO3-N), ±35–40% (PO4-P), ±83% (NH4-N) and ±12–15% (TS) of the measured reference value.
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32

Gunter, Bert, Christine Brideau, Bill Pikounis, and Andy Liaw. "Statistical and Graphical Methods for Quality Control Determination of High-Throughput Screening Data." Journal of Biomolecular Screening 8, no. 6 (December 2003): 624–33. http://dx.doi.org/10.1177/1087057103258284.

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High-throughput screening (HTS) is used in modern drug discovery to screen hundreds of thousands to millions of compounds on selected protein targets. It is an industrial-scale process relying on sophisticated automation and state-of-the-art detection technologies. Quality control (QC) is an integral part of the process and is used to ensure good quality data and mini mize assay variability while maintaining assay sensitivity. The authors describe new QC methods and show numerous real examples from their biologist-friendly Stat Server® HTS application, a custom-developed software tool built from the commercially available S-PLUS® and Stat Server® statistical analysis and server software. This system remotely processes HTS data using powerful and sophisticated statistical methodology but insulates users from the technical details by outputting results in a variety of readily interpretable graphs and tables. It allows users to visualize HTS data and examine assay performance during the HTS campaign to quickly react to or avoid quality problems.
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33

Alba-Fernández, María V., Francisco J. Ariza-López, José Rodríguez-Avi, and José L. García-Balboa. "Statistical Methods for Thematic-Accuracy Quality Control Based on an Accurate Reference Sample." Remote Sensing 12, no. 5 (March 3, 2020): 816. http://dx.doi.org/10.3390/rs12050816.

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The goal of this work is to present a set of statistical tests that offer a formal procedure to make a decision as to whether a set of thematic quality specifications of a product is fulfilled within the philosophy of a quality control process. The tests can be applied to classification data in thematic quality control, in order to check if they are compliant with a set of specifications for correctly classified elements (e.g., at least 90% classification correctness for category A) and maximum levels of poor quality for confused elements (e.g., at most 5% of confusion is allowed between categories A and B). To achieve this objective, an accurate reference is needed. This premise entails changes in the distributional hypothesis over the classification data from a statistical point of view. Four statistical tests based on the binomial, chi-square, and multinomial distributions are stated, to provide a range of tests for controlling the quality of product per class, both categorically and globally. The proposal is illustrated with a complete example. Finally, a guide is provided to clarify the use of each test, as well as their pros and cons.
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34

Childress, Ashley. "Statistical Methods for Quality Assurance: Basics, Measurement, Control, Capability, and Improvement, Second Edition." Journal of Quality Technology 51, no. 2 (February 8, 2018): 214–15. http://dx.doi.org/10.1080/00224065.2018.1404872.

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35

Jin, Yuan, Mark Carman, Ye Zhu, and Yong Xiang. "A technical survey on statistical modelling and design methods for crowdsourcing quality control." Artificial Intelligence 287 (October 2020): 103351. http://dx.doi.org/10.1016/j.artint.2020.103351.

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36

Benneyan, James C. "Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part I: Introduction and Basic Theory." Infection Control and Hospital Epidemiology 19, no. 3 (March 1998): 194–214. http://dx.doi.org/10.2307/30143442.

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37

Benneyan, James C. "Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part I: Introduction and Basic Theory." Infection Control and Hospital Epidemiology 19, no. 3 (March 1998): 194–214. http://dx.doi.org/10.1086/647795.

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38

Boyacioglu, Hülya, and Hayal Boyacioglu. "Application of environmetric methods to investigate control factors on water quality." Archives of Environmental Protection 43, no. 3 (September 1, 2017): 17–23. http://dx.doi.org/10.1515/aep-2017-0026.

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AbstractIn the study, environmetric methods were successfully performed a) to explore natural and anthropogenic controls on reservoir water quality, b) to investigate spatial and temporal differences in quality, and c) to determine quality variables discriminating three reservoirs in Izmir, Turkey. Results showed that overall water quality was mainly governed by “natural factors” in the whole region. A parameter that was the most important in contributing to water quality variation for one reservoir was not important for another. Between summer and winter periods, difference in arsenic concentrations were statistically significant in the Tahtalı, Ürkmez and iron concentrations were in the Balçova reservoirs. Observation of high/low levels in two seasons was explained by different processes as for instance, dilution from runoff at times of high flow seeped through soil and entered the river along with the rainwater run-off and adsorption. Three variables “boron, arsenic and sulphate” discriminated quality among Balçova & Tahtalı, Balçova & Ürkmez and two variables “zinc and arsenic” among the Tahtalı & Ürkmez reservoirs. The results illustrated the usefulness of multivariate statistical techniques to fingerprint pollution sources and investigate temporal/spatial variations in water quality.
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Jiang, Zhong Qiu. "The Intelligent Quality Control Technology System Based on the Integration Methods of SPC and EPC." Applied Mechanics and Materials 263-266 (December 2012): 839–42. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.839.

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SPC (statistical process control) and EPC (engineering process control) is the scientific methods of quality quality control and quality improvement. It is the difficult problems of quality control process for network manufacturing enterprise how to effectively solve the dynamic quality fluctuation monitoring and the fluctuation abnormal diagnostic analysis and timely process adjustments, this paper designs the intelligent quality control mode and function system architecture, the modes expatiates quality management network based on quality control network of the workshop level and enterprise class network, and researches the integration applications of statistical process control and enterprise ERP quality system, and applies J2EE technology to achieve the system organic combination of design and development.
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40

FIGIEL, KERRY, and MICHAEL FORBES. "The evolution of reel statistical methods." June 2019 18, no. 6 (July 1, 2019): 365–77. http://dx.doi.org/10.32964/tj18.6.365.

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Multiple statistical methods for calculating the variance partition analysis (VPA) of reel data have existed for decades. In the paper industry, VPA is also commonly known as reel statistics. VPA commonly consists of total variance (TOT) that is then divided into three components: cross direction (CD), machine direction (MD), and residual (RES). A common mathematical procedure is referred to as ANOVA (analysis of variance). TAPPI Standard Test Method T 545 “Cross-machine grammage profile measurement (gravimetric method)” addresses paper testing and includes the ANOVA equations that have also been used to analyze scanning data. In the 1990s, TAPPI published TIP 1101-01 “Calculation and partitioning of variance using paper machine scanning sensor measurements,” which contained simple formulas that were easy to implement and could be used by a novice to generate statistics on a spreadsheet. All involved quality control system (QCS) suppliers agreed to support this common method in their QCS. TIP 1101 was recently revised, and this paper concerns the analysis of data collected from a scanning sensor in a QCS and the creation of a common method for the calculation of reel statistics by TAPPI’s Process Control Division.
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Korenko, Maroš, Pavol Kaplik, Marian Bujna, and Miroslav Pristavka. "Statistical Process Control in Automotive Industry." Acta Technologica Agriculturae 16, no. 2 (June 1, 2013): 39–42. http://dx.doi.org/10.2478/ata-2013-0010.

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Abstract Organisations active in the market, regardless of the sphere of action, must constantly face pressures from an uncompromising competition. Therefore, if organisations wish to be successful in the market and constantly move forward, they must draw attention to the quality of products in the market. Statistical process control is the implementation of such statistical methods through which the production process is regulated and controlled to ensure the production of the highest possible amount of products that comply with requirements imposed on them.
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42

Miller, Robert B., Harrison M. Wadsworth, Kenneth S. Stephens, and A. Blanton Godfrey. "Modern Methods for Quality Control and Improvement." Journal of the American Statistical Association 82, no. 400 (December 1987): 1186. http://dx.doi.org/10.2307/2289410.

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43

Sorooshian, Shahryar. "Fuzzy Approach to Statistical Control Charts." Journal of Applied Mathematics 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/745153.

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After investigating the advantages and disadvantages of current methods of statistical process control, it becomes important to overcome the disadvantages and then use the advantages to improve a method for monitoring a process with categorical observations. An approach which considers uncertainty and vagueness is tried for this study; and for this purpose, fuzzy set theory is inevitable to use. So, a new approach based on fuzzy set theory is introduced in this research for monitoring attribute quality characteristics. This approach is then compared with the current related approach to see the difference in performance.
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44

Podur, Justin, David L. Martell, and Keith Knight. "Statistical quality control analysis of forest fire activity in Canada." Canadian Journal of Forest Research 32, no. 2 (February 1, 2002): 195–205. http://dx.doi.org/10.1139/x01-183.

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Statistical quality-control methods were used to detect significant changes in the mean and variance of the annual fire occurrence and area burned in Canada (1918–2000), Ontario (1917–2000), and northwestern Ontario (1917–2000). The quality-control chart method employed uses the first half of the record of a process as a baseline to test for significant changes in the mean or variance of the process in the second half of the record. Significant increases were detected in annual area burned and in fire occurrence in Canada, Ontario, and northwestern Ontario.
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45

Pchelkin, A. A. "Application of statistical methods for quality control of measurement processes in the enterprises of technical service of agroindustrial complex." Traktory i sel hozmashiny 83, no. 11 (November 15, 2016): 45–48. http://dx.doi.org/10.17816/0321-4443-66283.

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The attention to statistical methods of quality control is driven by their reliability in case of the small sample volumes in comparison with continuous control. The article shows the reasons of delaying of introduction of statistical methods in the enterprises of technical service of agroindustrial complex. The value of statistical quality control of test and measurement processes is pointed out. It is confirmed by the standardized requirements to the quality management and by the assessment of impact of this control on failures of products in operation. The use of statistical control allows to stabilize in more short timeframes the production on high-quality level. The analysis of standards and technic specifications for machinery production shows that the acceptance rules for machinery generally indicate the sample volume equal to 2% of batch of machines. This is only 1 machine of daily batch of 50 machines. At that, the risk of the fact that a consumer will receive defective products is 26%. The control of a single machine in the sample shows the imperfection of acceptance rules. Despite the availability of regulations on statistical quality control, it has not become widely used in practice in the field of domestic agricultural and automotive engineering and technical services. It is necessary to determine the problems of statistical quality control of test and measurement processes and the ways of its implementation; to establish the reasons for the lack of attention to statistical quality control. It is required to substantiate the ways for its widespread use in the technical service and production. As research techniques, it is proposed to apply the information retrieval, statistical methods of data collection, analogy methods and morphological analysis.
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Golubeva, Olesya, Alina Pogorelova, and Viktor Mirnyy. "Algorithmic Component of the New Statistical Quality Control Software Module." E3S Web of Conferences 210 (2020): 01008. http://dx.doi.org/10.1051/e3sconf/202021001008.

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Great development of information technologies largely determines the content of modern management, providing managers abilities for automatical data collection and processing, making decisions based on the use of a wide range of application software for various purposes. The use of quality management methods and tools is an important condition for product competitiveness. The possibility of using the software module as part of the developed information system is proposed. The article gprovides detailed description of statistical software module development for the purpose of product quality control. It is concluded that it is expedient and necessary to introduce this development into production.
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Петрова, Елена, Elena Petrova, Наталья Чернопольская, Natal'ya Chernopol'skaya, Надежда Маркова, and Nadezhda Markova. "APPLICATION OF STATISTICAL METHODS OF CONTROL FOR ESTIMATION OF STABILITY OF DAIRY PRODUCTION." Vestnik of Kazan State Agrarian University 12, no. 4 (January 18, 2018): 122–25. http://dx.doi.org/10.12737/article_5a5f0c5a0a0a66.83025820.

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The article presents the results of an estimation of defectiveness level of sour-milk production (on an example kefir), developed on one of dairy factories of region. To assess the level of defect, statistical methods of control are used - a checklist for the registration of defective products, a control p-chart and Ishikava diagram. The initial data were obtained at the stage of quality control of finished product and entered in the developed form of the control sheet for product defects. The most common defect in the production is established - the leakage of the package, which can be assessed visually, and not with the help of measuring instruments. For evaluation and subsequent analysis of the reasons for the appearance of the established defect, a control p-chart was used on an alternative basis, which makes it possible to control the quality of manufactured products by the number of defective products. As a result of the analysis of the obtained data, it was established that the technological process, from the point of view of statistical forecasting, is acceptable, but this does not mean a high production stability and does not exclude the appearance of defective products. The analysis of the main reasons for the emergence of unsealed packaging products, among them the qualification of personnel, the quality of the used materials and the conditions for their storage, the production environment, compliance with production technology, and others. A subsequent study of the impact of each of these factors will prevent the appearance of defective products and ensure the production of consistently high quality products.
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Pietrzak, Michał. "Statistical Disclosure Control Methods for Microdata from the Labour Force Survey." Acta Universitatis Lodziensis. Folia Oeconomica 3, no. 348 (June 22, 2020): 7–24. http://dx.doi.org/10.18778/0208-6018.348.01.

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The aim of this article is to analyse the possibility of applying selected perturbative masking methods of Statistical Disclosure Control to microdata, i.e. unit‑level data from the Labour Force Survey. In the first step, the author assessed to what extent the confidentiality of information was protected in the original dataset. In the second step, after applying selected methods implemented in the sdcMicro package in the R programme, the impact of those methods on the disclosure risk, the loss of information and the quality of estimation of population quantities was assessed. The conclusion highlights some problematic aspects of the use of Statistical Disclosure Control methods which were observed during the conducted analysis.
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Tillett, H. E., and N. F. Lightfoot. "Preliminary Statistical Assessment of UK Water Quality Control Trials." Water Science and Technology 24, no. 2 (July 1, 1991): 57–60. http://dx.doi.org/10.2166/wst.1991.0030.

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In the UK the Public Health Laboratory Service distributes simulated water samples with low or moderate counts of organisms, usually coliforms. Sometimes these include small numbers of E. coli. Statistical analysis can be used to assess the performance of laboratories. The reasoning behind the statistical methodology must be made clear to participants. In any one trial the counts recorded by participating laboratories can be highly variable, but this may merely reflect the distribution of organisms between samples. Even with carefully prepared batches of water samples, where the aim is for random distribution of the organisms, there is no guarantee that randomness is achieved and greater variation may be observed. It would be unfair to criticise laboratories returning a result at the extreme ends of the range of counts without further evidence. After a series of trials it is possible to assess whether there are any laboratories which are returning extreme low (or high) counts more often than would be expected by chance. As an example a non-parametric test (Cochran's Q) is used to demonstrate, from a series of ten trials, whether there were any laboratories giving poor results. With coliform organism counts an arbitrary definition of low or high counts is used. It is important that the statistical assessment uses an approach which aims to detect poor performance when it is present but not when it is absent. The practice of automatically condemning a predetermined proportion (e.g. 5%) seems unfair, since all laboratories may be performing adequately or a large proportion may be inadequate. The assessment demonstrated here is designed to cope with mixed methods - dilution series or direct counts.
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Allemani, Claudia, Rhea Harewood, Christopher J. Johnson, Helena Carreira, Devon Spika, Audrey Bonaventure, Kevin Ward, Hannah K. Weir, and Michel P. Coleman. "Population-based cancer survival in the United States: Data, quality control, and statistical methods." Cancer 123 (December 5, 2017): 4982–93. http://dx.doi.org/10.1002/cncr.31025.

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