
Main / Vladimir Nikolaevich Klyachkin
Author: "Vladimir Nikolaevich Klyachkin"
Vladimir Nikolaevich Kliachkin, Ulyanovsk State Technical University, Doctor of Engineering; graduated from the Mechanical Faculty of Ulyanovsk Polytechnic Institute; Professor at the Department of Applied Mathematics and Informatics of Ulyanovsk State Technical University; an author of scientific works in the field of reliability and statistical methods. [email: v_kl@mail.ru]V. Kliachkin, Ekaterina Aleksandrovna Zentsova, Ulyanovsk State Technical University, graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; Postgraduate Student at the Department of Applied Mathematics and Informatics of Ulyanovsk State Technical University; an author of scientific papers in the field of statistical process control. [email: e_zentsova@mail.ru]E. Zentsova


Design of an Adaptive Control Scheme for Multivariate Statistical Process Control
Statistical control of a technological process is used in order to guarantee the required quality level by implementing timely corrective actions when the process is instable. Quality of a product manufactured in a technological process is usually measured by multiple quality characteristics, part of them are correlated. Statistical control is carried out separately for groups of correlated and independent characteristics. Independent quality characteristics of a technological process can be controlled with standard Shewhart’s control charts. In order to monitor a set of correlated characteristics, a multivariate Hotelling’s control chart is used. The main purpose of this chart is to observe multivariate process settings level. This chart quickly detects large shifts in the multivariate process mean and often ignores small ones. In order to improve its performances in detecting small shifts in the mean vector, the adaptive control scheme design is proposed. Its parameters change according to predictions based on the current process status observations. The characteristics of different adaptive control schemes are designed in a standardized fashion to ensure the comparison of these schemes is meaningful and unbiased. Adaptive control scheme, hotelling’s control chart, markov chains.



Sections: Mathematical modeling
Subjects: Mathematical modeling. 
Vladimir Nikolaevich Kliachkin, Ulyanovsk State Technical University, Doctor of Engineering; graduated from the Mechanical Faculty of Ulyanovsk Polytechnic Institute; Professor at the Department of Applied Mathematics and Informatics of Ulyanovsk Polytechnic University; an author of scientific works in the field of reliability and statistical methods. [email: v_kl@mail.ru]V. Kliachkin, Irina Nikolaevna Karpunina, Ulyanovsk Institute of Civil Aviation named after Chief Marshal of Aviation B.P. Bugaev, Candidate of Engineering, Associate Professor; graduated from Moscow Aviation Institute; Associate Professor at the Department of General Professional Disciplines at Ulyanovsk Institute of Civil Aviation named after Chief Marshal of Aviation B.P. Bugaev; interested in dynamics and strength of machines, reliability. [email: karpunina53@yandex.ru]I. Karpunina, Mariia Konstantinovna Fedorova, Ulyanovsk State Technical University, graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; interested in computeraided technologies of statistical data analysis. [email: mashulka3031_94@mail.ru]M. Fedorova


Evaluation of the Computer Temperature Regime Stability
The temperature regime significantly affects the durability of the computer. Ensuring stability of the computer functioning considers the stability of the heating temperature of the main elements that should not exceed the specified values. The article discusses issues related to the timely warning about a possible violation of the temperature regime stability. In order to diagnose stability, the multivariate statistical control methods are proposed to use. Evaluation of stability is carried out with the use of two criteria: stability of the temperature average level and their dispersion. Independent parameters can be controlled with the use of standard shewhart charts. The algorithms on the basis of Hotelling statistics (for assessing stability of the middlelevel temperature measurement process) and generalized variance (for evaluation of the dispersion process stability) are used for correlated parameters. The efficiency of these algorithms can be enhanced through the analysis of nonrandom structures on the control charts, the use of the warning border, as well as application of modifications based on the cumulative amounts or moving averages weighted exponentially. The multivariate statistical technique of the computer temperature regime control including monitoring in the context of a welldeveloped process for a training sample in order to separate the parameters controlled for a group of independent and correlated ones, process analysis for assessment of control characteristics and continuous monitoring of the process with the construction of Hotelling charts and generalized dispersion with identifying possible violations of the process based on the presence of significant structures and the use of a warning border. This technique is illustrated by the example of five computer temperature regime parameters control. Stability, temperature, hotelling algorithm, warning limit, generalized variance, control chart.



Sections: Mathematical modeling
Subjects: Mathematical modeling, Automated control systems, Electrical engineering and electronics. 
Vladimir Nikolaevich Klyachkin, Ulyanovsk State Technical University, Doctor of Engineering; graduated from the Faculty of Mechanics at Ulyanovsk Polytechnic Institute; Professor at the Department of Applied Mathematics and Informatics at Ulyanovsk State Technical University; an author of scientific papers in the field of reliability and statistical methods. [email: v_kl@mail.ru]V. Klyachkin, Iurii Andreevich Kravtsov, Ulyanovsk State Technical University, graduated from the Faculty of Physics and Technology at Ulyanovsk State University, PostGraduate Student at the Department of Applied Mathematics and Informatics at Ulyanovsk State Technical University; an author of scientific papers in the field of statistical process control. [email: ukravtsov@rambler.ru]I. Kravtsov, Dmitrii Anatolievich Zhukov, Ulyanovsk State Technical University, a fifthyear student of a specialty in Applied Mathematics at Ulyanovsk State Technical University; his research interests  statistical diagnostic methods of technical objects. [email: zh.dimka17@mail.ru]D. Zhukov


Evaluation of Object Status Diagnosing Efficiency to Nonrandom Structures Existance on the Hotelling’s Chart
The status of a technical object is often considered emergency if the stability of its operation is broken. multivariate Hotelling’s charts can be used for diagnostics of stability disruption on set of the correlated parameters. The basic criterion of the process stability in this case is the absence of the points above control limit on the chart. However, this criterion does not allow to find out process disruption promptly. For the purpose of increasing sensitivity of the chart to possible failures, it is offered to reveal the presence of nonrandom structures on the chart (the arrangement of points that indicates the process disruption). One more possible way to increase the effectiveness of the control is to use the warning border (the several points that hit in a range between warning and control limits and testify to the process disruption).The effectiveness of these methods is investigated analytically and with the use of statistical tests. The sensitivity substantial increase to possible process disruption is shown by example of the concrete technological process of manufacturing a cover of the sensor of aerodynamic corners. Process stability, hotelling's chart, nonrandom structures, warning border, efficiency of the control.



Sections: Mathematical modeling
Subjects: Mathematical modeling. 
Vladimir Nikolaevich Klyachkin, Ulyanovsk State Technical University, Doctor of Engineering; graduated from the Faculty Mechanics at Ulyanovsk Polytechnic Institute; Professor at the Department of Applied Mathematics and Informatics at Ulyanovsk State Technical University; an author of scientific papers in the field of reliability and statistical methods. [email: v_kl@mail.ru]V. Klyachkin, Tatiana Igorevna Svyatova, Ulyanovsk State Technical University, graduated from the Faculty of Economics and Mathematics at Ulyanovsk State Technical University; PostGraduate Student at the Department of Applied Mathematics and Informatics at Ulyanovsk State Technical University; an author of scientific papers in the field of the statistical process control. [email: tatyana.krasko@rambler.ru]T. Svyatova, Iurii Andreevich Kravtsov, Ulyanovsk State Technical University, graduated from the Faculty of Physics and Technology at Ulyanovsk State University; PostGraduate Student at the Department of Applied Mathematics and Informatics at Ulyanovsk State Technical University; an author of scientific papers in the field of statistical process control. [email: ukravtsov@rambler.ru]I. Kravtsov


Workflow Data Modeling for Analysis of Multidimensional Statistical Control Effectiveness
The multivariate statistical control of the technological process is usually spent with the use of the Hotelling’s chart. If necessary, the generalized dispersion card is used in the process control disruption. Efficiency of the control is evaluated by means of average run length. It is the number of observations from the moment of the process disruption till the moment of the detection of this disruption.For the purpose of increasing the sensitivity of the control to possible disruptions various approaches are used: the analysis of the special kind of structures on the chart, application of the warning border, modification of charts on the basis of algorithms of cumulative sums and exponentially weighted moving averages and others. Thus the average run length sometimes can be determined analytically and more often it has to be estimated with the results of statistical tests. To carry out such tests the sequence of data vectors of technological process is modelled and also various kinds of disruptions are set. Data modeling is offered to implement on the basis of the multivariate normal disruption with the subsequent parameters check. Spasmodic displacement of an average process level and its trend and also spasmodic and gradual increase of the process dispersion are guarded as the basic process disruptions. Multivariate control chart, statistical tests, average run length, sequence of data, process disruption.



Sections: Mathematical modeling
Subjects: Mathematical modeling. 
Valerii Pavlovich Kiselevich, Production  Technical Director at Concern ‘MorinformsystemAgat’ JSC, in Moscow, Candidate of Chemistry; graduated from the Faculty of Instrument Egneering at Leningrad Institute of Aircraft Instrumentation; Deputy General Director on Production  Technical Director at Concern ‘MorinformsystemAgat’ JSC, in Moscow; an author of articles and patents in the field of quality assurance and inspection of electric devices. [email: kiselevich_vp@concernagat.ru]V. Kiselevich, Vladimir Nikolaevich Klyachkin, Ulyanovsk State Technical University, Doctor of Engineering; graduated from the Mechanical Faculty of the Ulyanovsk Polytechnic Institute; Professor at the Department of Applied Mathematics and Informatics of the Ulyanovsk State Technical University; an author of scientific papers in the field of reliability, service life and mathematics. [email: v_kl@mail.ru]V. Klyachkin, Vladimir Vasilyevich Sukhov, Joint Stock Company Concern ‘MorininformsystemAgat’ (Moscow), Candidate of Engineering; graduated from the Mechanical Engineering Faculty of Bauman Moscow Technical University with the specialty in Radiomechanical Devices; Chief of Engineering Department at the Joint Stock Company Concern ‘MorininformsystemAgat’ (Moscow); a patent holder and an author of articles in the field of reliability, testing, and calculations of dynamics and strength of radio equipment, vibroinsulation system, vibroacoustic and sound characteristics, thermal conditions. [email: vsuhov051@yandex.ru]V. Sukhov


Computer System Resource Posttest Prediction
The article deals with problems of the computer system resource posttest prediction derived from accelerated tests under different influences. Resources estimation methods are divided into four groups: statistical, deterministic, physicstatistics, and expert methods. The first three are the most commonly used methods. The analytical methods for accounting of effect of these influences on resource were made only for individual factors, while not always taken into account particulars of random influences. The methods of calculating the service life related with the fatigue strength are known. There were analyzed external influences that have the greatest impact on the design of the device so that to develop an effective method for estimating the resource. On the basis of analysis and accelerated testing, the method of estimating the mean and gammapercentile resource based on the distribution of time to failure, the parameters of which are determined by calculation, has been proposed. The tests were carried out on a large group of modules for each selected action. There was given an example of calculation of the system as the serial communication subsystems, where each of subsystems is under the influence of the vibration, temperature, power supply ON and OFF. Prediction, computer system, accelerated testing, statistical methods, the weibull distribution.



Sections: Mathematical modeling
Subjects: Mathematical modeling, Electrical engineering and electronics. 
