Вимірювальна техніка та метрологія

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    Застосування методу серій для дослідження взаємної кореляції спостережень
    (Видавництво Львівської політехніки, 2016) Дорожовець, Михайло; Никипанчук, Олена; Національний університет “Львівська політехніка”
    Запропоновано метод обчислення автокореляції, а також ефективної кількості спостережень. Для генерування корельованих спостережень використовується метод рухомого середнього. За допомогою методу серій можливе спрощене обчислення ефективної кількості спостережень для визначення стандартної непевності середнього значення корельованих спостережень. Предложен метод вычисления автокорреляции, а также эффективного числа наблюдений. Для генерирования коррелированных наблюдений используется метод подвижного среднего. С помощью метода серий возможно упрощенное вычисление эффективного числа наблюдений для определения стандартной неопределенности среднего значения коррелированных наблюдений. When processing the results of measurements big role important presence of correlation values. To find the standard uncertainty need to know the effective number of uncorrelated observations. No correlation can consider could lead to incorrect evaluation of the standard uncertainty of the mean. Not always known autocorrelation function monitoring, and evaluation of the autocorrelation function on observations characterized by low accuracy, which can lead to incorrect finding effective number. There are indirect methods of evaluating the impact assessment on observations correlation standard deviation. This method is recorded sample of N divided into k sub-samples (groups) up to n samples each (N = n · k). Each subsample are partial mean and variance estimation, and find the settings for the entire sample. Then compare the ratio of the variance between groups and within the group. Using the F distribution at a significance level α determined whether the observations are correlated or not. These methods are quite complex and require significant additional computing. The purpose of research is to study simple method of testing autocorrelation and consideration in calculating the Neff. The proposed method is based on calculating the number of series. Series is a sequence of observed values equal before which or after which the values observed are another category or no supervision at all. Set the number of series or observation results are correlated or not. To determine whether correlated observations required to determine the median of the sample and calculate the number of deviations from the median values. Research performed by theMonte Carlo. For research use two types of observations: first – with uncorrelated observations, the second – generated correlated observations, including the method ofmoving average. To find the index of correlation function used exponential autocorrelation function. An effective dependence theoretical number and effective number determined by the method episodes from different bias moving average on a constant number of observations. Based on these studies show that increasing the number of observations (N> 50) to simplify the calculation of the possible number of effective using the method of series. At least 50 the number of observations can be effective calculating numbers with a small biasmoving average.To investigate the cross-correlation of observations of the method is appropriate series and simplifies the calculation of the standard uncertainty.
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    Встановлення зв’язків між вимогами замовника та показниками виробу з використанням методу QFD та Fuzzy Logic
    (Видавництво Львівської політехніки, 2015) Бойко, Тарас; Мельник, Володимир
    Запропоновано алгоритм, який використовує розгортання функції якості для перетворення вимоги замовника на характеристики властивостей виробів, їх матеріалів, вимоги виробничих процесів, нормативних документів тощо. Алгоритм використовує також нечітку логіку для підвищення точності оцінювання вимог, оскільки забезпечує математичні операції із лінгвістичними змінними. Запропонована практична реалізація алгоритму на прикладі напірної поліетиленової труби для підземних газогонів. Сформовано структуру показників, що характеризують перелічені складові й можуть бути використані для діагностування готового виробу. Предложен алгоритм, который использует развертывание функции качества для преобразованиятребования заказчика в характеристики свойств изделий, их материалов, требования производственных процессов, нормативных документов итому подобное. Алгоритм используеттакже нечеткую логику для повышения точности оценкитребований, поскольку обеспечивает математические операции с лингвистическими переменными. Предложенная практическая реализация алгоритма на примере напорной полиэтиленовойтрубы для подземных газопроводов. Сформирована структура показателей, характеризующих перечисленные составляющие, которые могут быть использованы для диагностики готового изделия. Now for the developed countries of the world community is inherent to ensure quality by organizational and management measures, namely development, implementation, certification and continuous improvement of quality systems. Proceedings of the outstanding scientists of the twentieth century W. Shewhart, W. E. Deming, Joseph M. Juran (first introduced the concept of “quality control”), F. Crosby, K. Ishikawa, Armand V. Feigenbaum, H. Taguchi, T. Seyfi formed a modern strategy based on the application of quality management systems (QMS). Within an effective QMS in the company created an environment so that products could be of high quality. In particular, the main criterion for evaluating the QMS is to have a continuous improvement process that should lead to greater customer satisfaction, apparently due to the continuous improvement of products, or, in other words, increase its quality. Therefore, the development of new methods of generating quality factors and their importance for evaluating the quality of products is an urgent task. Mixed-economic approach to forming index of products quality using standard economic “tools” for enhancing the competitiveness of enterprises, such as benchmarking, reverse engineering analysis and quality function deployment is proposed. Overall benchmarking is the process of finding a standard or reference more cost effective enterprise-rival to compare with their own and adopting best practice. The essence of the present interpretation of benchmarking can be formulated as nonstop systematic search and implementation of best practices that will lead the organization to a more perfect form. Therefore it is considered that benchmarking is an effective tool for determining the position of your company compared to others of a similar size and / or scope of activities and organizations. To elucidate the causes of advantages competitors perform the so-called reverse engineering analysis, which is also a form of benchmarking. The method is aimed at answering the following question – how to provide with high performance products? The results of engineering analysis are also presented in the form of matrices that are recommended to be built separately for components, materials, methods of manufacture and assembly, although it complicates their comparison. The weak point of engineering analysis is the lack of algorithm for continuous communication engineering parameters of the product with manufacturing operations and production requirements. This algorithm can be realized on the base of QFD (quality function deployment), in fact, it is a technology of engineering design products and processes for their manufacture and “converts” the wishes of consumers in the technical specifications of the product as well as process parameters of its production. Besides, QFD allows to assess the importance of consumer product options, linking them to the requirements of customers. Moreover consumers’ wishes are taken into consideration for their “transformation” into the measured parameters by using tools of qualimetry. The basis of the quality function deployment - QFD, or as it is called simultaneous engineering method is the use of a series of two-or even three-dimensional tables, matrices, socalled “houses of quality”. These matrices allow consumers to link requirements to the quality of the performance of the product, product performance link with characteristics of engineering components, component specifications link with manufacturing operations and production operations to the requirements of production. Thus, it is preferable to use four table-matrix. As a result, it can be stated that the method QFD provides: – the relationship between the demands of consumers, product specifications, options of its functional subsystems and their components at all stages of development; – way of shifting consumer demands in a controlled set of features (most of this activity requires benchmarking) and requirements for manufacturing technology products.