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Статті в журналах з теми "Physical-statistical model of reliability"

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Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Rajeevan, A. K., P. V. Shouri, and Usha Nair. "A Reliability Based Model for Wind Turbine Selection." International Journal of Renewable Energy Development 2, no. 2 (June 17, 2013): 69–74. http://dx.doi.org/10.14710/ijred.2.2.69-74.

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A wind turbine generator output at a specific site depends on many factors, particularly cut- in, rated and cut-out wind speed parameters. Hence power output varies from turbine to turbine. The objective of this paper is to develop a mathematical relationship between reliability and wind power generation. The analytical computation of monthly wind power is obtained from weibull statistical model using cubic mean cube root of wind speed. Reliability calculation is based on failure probability analysis. There are many different types of wind turbinescommercially available in the market. From reliability point of view, to get optimum reliability in power generation, it is desirable to select a wind turbine generator which is best suited for a site. The mathematical relationship developed in this paper can be used for site-matching turbine selection in reliability point of view.

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2

Si, Wujun, Qingyu Yang, and Xin Wu. "A physical–statistical model of overload retardation for crack propagation and application in reliability estimation." IIE Transactions 48, no. 4 (September 22, 2015): 347–58. http://dx.doi.org/10.1080/0740817x.2015.1078525.

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3

Kim, Youngjoo, Okjoo Choi, Moonzoo Kim, Jongmoon Baik, and Tai-Hyo Kim. "Validating Software Reliability Early through Statistical Model Checking." IEEE Software 30, no. 3 (May 2013): 35–41. http://dx.doi.org/10.1109/ms.2013.24.

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4

Saccomanno, F. F., S. A. Nassar, and J. H. Shortreed. "Reliability of Statistical Road Accident Injury Severity Models." Transportation Research Record: Journal of the Transportation Research Board 1542, no. 1 (January 1996): 14–23. http://dx.doi.org/10.1177/0361198196154200103.

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The reliability of different statistical road accident severity models is assessed using a number of criteria, such as goodness of fit, robustness of risk factor coefficients, and intuitive acceptability and consistency of output. The results of this analysis suggest that model reliability is not sensitive to the number of injury classes specified in the model or to the level of model aggregation. All models consistently identified the same risk factors as explaining most of the variation in injury experience in the data. These factors are (a) dynamics of the accident, (b) seating position of occupant, (c) use of seat belts, and (d) age of occupant involved. There is no indication that a significant transfer of error takes place from one severity level to another in a sequential model structure. The results suggest that reliability of statistical road accident severity models depends primarily on the accuracy of information provided in the accident data.

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5

Zambon, Ivan, Anja Vidović, and Alfred Strauss. "Reliability of Existing Concrete Structures Determined with Physical Models - Carbonation Induced Corrosion." Solid State Phenomena 259 (May 2017): 255–60. http://dx.doi.org/10.4028/www.scientific.net/ssp.259.255.

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The main goal of transportation infrastructure management is to optimize the use of infrastructure in the most beneficiary way while respecting the predefined requirements. One of the crucial parts in management strategy is the prediction of behaviour of vital transportation elements. Used prediction models should accurately describe the process of degradation and allow forecasting of structural condition by considering environment, usage and maintenance actions. Deterioration models can be divided into mathematical (statistical), physical and empirical models. Statistical models are based on data that describe condition of structure, such as for example condition rating. Physical models describe damage-causing processes and empirical models are experience based. The focus of this paper is to present the physical model of carbonation in assessment of performance of existing reinforced concrete structures in transportation networks. Assessment is done through determining the probability of limit state of depassivation. In order to determine the carbonation without testing, a special attention has to be given to environmental and material parameter identification. Herein, the identification takes into account weather specifics and construction practice in Austria. Finally, the reliability of existing reinforced concrete structures for combination of different exposure classes and material characteristics is analysed. Based on the analysis of reliability, the carbonation nomogram for engineering use is presented, showing the reliability indices β for the service life of 50 years.

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6

VanDerHorn, Eric, and Sankaran Mahadevan. "Bayesian model updating with summarized statistical and reliability data." Reliability Engineering & System Safety 172 (April 2018): 12–24. http://dx.doi.org/10.1016/j.ress.2017.11.023.

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7

Ding, Jie, and Asen Asenov. "Reliability-Aware Statistical BSIM Compact Model Parameter Generation Methodology." IEEE Transactions on Electron Devices 67, no. 11 (November 2020): 4777–83. http://dx.doi.org/10.1109/ted.2020.3026614.

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8

Chen, Chunhong, and Yanjie Mao. "A Statistical Reliability Model for Single-Electron Threshold Logic." IEEE Transactions on Electron Devices 55, no. 6 (June 2008): 1547–53. http://dx.doi.org/10.1109/ted.2008.922856.

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9

Agustin, Marcus A. "Statistical properties of a system reliability estimator using the Littlewood software reliability model." Journal of Applied Probability 40, no. 03 (September 2003): 766–78. http://dx.doi.org/10.1017/s0021900200019690.

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This paper considers a competing risks system withppieces of software where each piece follows the model by Littlewood (1980) described as follows. The failure rate of a piece of software relies on the residual number of bugs remaining in the software where each bug produces failures at varying rates. In effect, bugs with higher failure rates tend to be observed earlier in the testing period. Tasks are assigned to the system and the task completion times as well as the software failure times are assumed to be independent of each other. The system is observed over a fixed testing period and the system reliability upon test termination is examined. An estimator of the system reliability is presented and its asymptotic properties as well as finite-sample properties are obtained.

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10

Agustin, Marcus A. "Statistical properties of a system reliability estimator using the Littlewood software reliability model." Journal of Applied Probability 40, no. 3 (September 2003): 766–78. http://dx.doi.org/10.1239/jap/1059060901.

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Анотація:

This paper considers a competing risks system with p pieces of software where each piece follows the model by Littlewood (1980) described as follows. The failure rate of a piece of software relies on the residual number of bugs remaining in the software where each bug produces failures at varying rates. In effect, bugs with higher failure rates tend to be observed earlier in the testing period. Tasks are assigned to the system and the task completion times as well as the software failure times are assumed to be independent of each other. The system is observed over a fixed testing period and the system reliability upon test termination is examined. An estimator of the system reliability is presented and its asymptotic properties as well as finite-sample properties are obtained.

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11

SAWADA, KIYOSHI, and HIROAKI SANDOH. "A SUMMARY OF SOFTWARE RELIABILITY DEMONSTRATION TESTING MODELS." International Journal of Reliability, Quality and Safety Engineering 06, no. 01 (March 1999): 65–80. http://dx.doi.org/10.1142/s0218539399000085.

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This paper summarizes models for software reliability demonstration testing (SRDT). The models are briefly classified into three types: (1) continuous models, (2) discrete models and (3) models considering damage sizes of software failures. Under the continuous models, the software product of interest is tested for time t and is accepted if the number of software failures in the test does not exceed a prespecified integer s. The values of design variables, t and s are determined based on (i) the concept of a statistical test (a statistical model) and (ii) the Kullback–Leibler information (a K–L model). The K–L model has less parameters to be prespecified than the statistical model. Under the discrete models for SRDT, the software of interest is tested with n input data sets and is accepted if the number of input data sets causing software failures in the test does not exceed a prespecified integer c. A statistical model as well as a K–L model is described for the discrete models. Neither continuous nor discrete models in the above take the damage size of software failures into consideration. The third type of models are continuous and discrete models which consider the cumulative damage size caused by software failures as well as the number of software failures in the test.

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12

Xie, Li Yang, Wen Xue Qian, and Ning Xiang Wu. "Reliability Model for Complex Mechanical Component Design." Applied Mechanics and Materials 365-366 (August 2013): 28–31. http://dx.doi.org/10.4028/www.scientific.net/amm.365-366.28.

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Taking into account the uncertainty in material property and component quality, a complex mechanical component such as a gear should be treated as a series system instead of a component when evaluating its reliability, since there exist many sites of equal likelihood to fail. Besides, conventional system reliability model is not applicable to such a system because of the statistical dependence among the failures of the every element (damage site). The present paper presents a model to estimate complex mechanical component reliability by incorporating order statistic of element strength into load-strength interference analysis, which can deal with multiple failure mechanisms, reflect statistical dependence among element failure events and that among different failure modes.

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13

Mathur, Nirbhay, Vijanth Sagayan Asirvadam, and Azrina Abd Aziz. "Mechanical Damage Assessment for Pneumatic Control Valves Based on a Statistical Reliability Model." Sensors 21, no. 10 (May 11, 2021): 3307. http://dx.doi.org/10.3390/s21103307.

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A reliability assessment is an important tool used for processing plants, since the facility consists of many loops and instruments attached and operated based on other availability; thus, a statistical model is needed to visualize the reliability of its operation. The paper focuses on the reliability assessment and prediction based on the existing statistical models, such as normal, log-normal, exponential, and Weibull distribution. This paper evaluates and visualizes the statistical reliability models optimized using MLE and considers the failure mode caused during a simulated process control operation. We simulated the failure of the control valve caused by stiction running with various flow rates using a pilot plant, which depicted the Weibull distribution as the best model to estimate the simulated process failure.

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14

LV, Z., L. Milor, and S. Yang. "Statistical model of NBTI and reliability simulation for analogue circuits." Microelectronics Reliability 52, no. 9-10 (September 2012): 1837–42. http://dx.doi.org/10.1016/j.microrel.2012.06.039.

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15

Hellmich, Mario. "Statistical inference of a software reliability model by linear filtering." Journal of Statistics and Management Systems 19, no. 2 (March 3, 2016): 163–81. http://dx.doi.org/10.1080/09720510.2014.961801.

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16

Gaudoin, O., and J. L. Soler. "Statistical analysis of the geometric de-eutrophication software-reliability model." IEEE Transactions on Reliability 41, no. 4 (1992): 518–24. http://dx.doi.org/10.1109/24.249578.

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17

Prourzin, Vladimir A. "General physical principle in dynamic model of reliability." International Journal of Risk Assessment and Management 20, no. 4 (2017): 322. http://dx.doi.org/10.1504/ijram.2017.087902.

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18

Prourzin, Vladimir A. "General physical principle in dynamic model of reliability." International Journal of Risk Assessment and Management 20, no. 4 (2017): 322. http://dx.doi.org/10.1504/ijram.2017.10008740.

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19

Zeifman, Michael I., and Dov Ingman. "Statistical Aspects of Lifetime in the Presence of UV Radiation." Journal of Electronic Packaging 125, no. 1 (March 1, 2003): 1–3. http://dx.doi.org/10.1115/1.1498263.

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The reliability function of a component cannot be satisfactorily estimated from experiments, because: (i) an accurate estimation of the lifetime distribution tails, controlling the most important domain of high reliability, requires a very large sample and (ii) reliability tests under normal operational conditions are necessarily very lengthy. Hence the urgent need for a physical model for component lifetime statistics. The paper presents an application of the recently developed model for damage accumulation in polymeric materials to the long-term constant stress rupture experiments on Kevlar Composite, which is widely used in fiber optics. The strong dependence of the experimentally observed distribution shape on the load applied to a component has been previously explained in the framework of two different damage mechanisms: kinetic crack growth and chemical deterioration; the resultant 3-parameter lifetime distribution was predicted to be essentially non-Weibull. The proposed model, based on a single micro-mechanical damage mechanism, leads to a 2-parameter Weibull lifetime distribution with the shape parameter depending on the applied load by a simple inverse power law. Both distribution models were fitted to experimental lifetime data for different stress levels and the corresponding goodness of fit was compared by the usual likelihood ratio test. The proposed model describes the experimental data better — especially in the most important domain of low stress and long (of the order of years) lifetime. The model is physically sound and permits improved design of the accelerated tests and more accurate interpretation of their results, and finally quantitative prediction of the reliability function of the loaded polymeric component.

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20

Urbina, Angel, and Thomas Paez. "Statistical Validation of Structural Dynamics Models." Journal of the IEST 46, no. 1 (September 14, 2003): 141–48. http://dx.doi.org/10.17764/jiet.46.1.f430423634885g67.

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There is an increasing reliance in the engineering community on the use of mathematical models to characterize physical system behavior. This is happening even though mathematical models rarely simulate real system behavior perfectly. Due to this reliance, we require objective, well-founded mathematical techniques for model validation. This paper develops a formal approach to the validation of mathematical models of structural dynamics systems. It uses a probabilistic/statistical approach to the characterization of an important measure of behavior of dynamic systems subjected to random excitations, and seeks to validate a mathematical model in a statistical sense. An example is presented.

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21

Ye, Ronghui, Jun Kong, Chengji Shen, Jinming Zhang, and Weisheng Zhang. "An Alternative Statistical Model for Predicting Salinity Variations in Estuaries." Sustainability 12, no. 24 (December 21, 2020): 10677. http://dx.doi.org/10.3390/su122410677.

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Accurate salinity prediction can support the decision-making of water resources management to mitigate the threat of insufficient freshwater supply in densely populated estuaries. Statistical methods are low-cost and less time-consuming compared with numerical models and physical models for predicting estuarine salinity variations. This study proposes an alternative statistical model that can more accurately predict the salinity series in estuaries. The model incorporates an autoregressive model to characterize the memory effect of salinity and includes the changes in salinity driven by river discharge and tides. Furthermore, the Gamma distribution function was introduced to correct the hysteresis effects of river discharge, tides and salinity. Based on fixed corrections of long-term effects, dynamic corrections of short-term effects were added to weaken the hysteresis effects. Real-world model application to the Pearl River Estuary obtained satisfactory agreement between predicted and measured salinity peaks, indicating the accuracy of salinity forecasting. Cross-validation and weekly salinity prediction under small, medium and large river discharges were also conducted to further test the reliability of the model. The statistical model provides a good reference for predicting salinity variations in estuaries.

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22

Karbasian, Mahdi, and Ramin Rostamkhani. "Achieving productive reliability through applying statistical distribution functions." International Journal of Quality & Reliability Management 37, no. 8 (November 25, 2019): 1125–49. http://dx.doi.org/10.1108/ijqrm-11-2018-0298.

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Purpose The purpose of this paper is to find the proper statistical distribution function, which can cover the failure time of a single machine or a group of machines. To this end, an innovative program is written in an Excel software, capable of assessing at least six statistical distribution functions. This research study intends to show the advantages of applying statistical distribution functions in an integrated model format to create or increase productive reliability machines. Productive reliability is a simultaneous combination of efficiency and effectiveness in reliability. Design/methodology/approach The method of theoretical research methodology comprises data collection tools, reference books and articles in addition to exploiting written reports of the Iranian Center for Defence’s Standards. The practical research method includes deploying and assessing the proposed model for a selected machine (in this case a computerized numerical control machine). Findings A comprehensive program in an Excel software having the capability of assessing at least six statistical distribution functions was developed to find the most efficient option for covering the failure times of each machine in the shortest time with the highest precision. This is regarded as the most important achievement of the present study. Furthermore, the advantages of applying the developed model are discussed and a large group of which have direct influences on the productivity of equipment reliability. Originality/value The originality of the research was ascertained by managers and experts working in maintenance issues at the different levels of the Defense Industries Organization.

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23

Yanmaz, A. Melih, and Ozgur Cicekdag. "Composite reliability model for local scour around cylindrical bridge piers." Canadian Journal of Civil Engineering 28, no. 3 (June 1, 2001): 520–35. http://dx.doi.org/10.1139/l01-009.

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Bridge scour is an extremely complex phenomenon because of the random characteristics of sediment laden flow in close proximity to piers and abutments. This occurrence leads to high uncertainties and unavoidable risk in bridge pier and abutment design. In this study, a composite reliability model is developed for the reliability assessment of bridge pier scour using static resistance loading interference. Based on the physical interpretation of the phenomenon and a statistical analysis of the available information, the relative maximum scour depth (corresponding to the minimum required relative pier footing elevation) and the linear combination of the relative approach flow depth and flow Froude number are defined as the system resistance and external loading, respectively. By examining a set of laboratory and field data, a two-parameter bivariate lognormal distribution is found to represent the joint probability density function of resistance and loading. Reliability expressions are developed in terms of resistance. Use of the model is illustrated in a practical application in which a relationship is obtained between the reliability and safety factors under various return periods. This information is of importance in decision making.Key words: reliability, bridge pier, scour, resistance, loading, safety factor, return period.

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24

Abascal, Ana J., Sonia Castanedo, Raul Medina, and Maria Liste. "Analysis of the reliability of a statistical oil spill response model." Marine Pollution Bulletin 60, no. 11 (November 2010): 2099–110. http://dx.doi.org/10.1016/j.marpolbul.2010.07.008.

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25

Ren, X. D., Y. K. Zhang, D. W. Jiang, T. Zhang, and G. F. Sun. "A model for reliability and confidence level in fatigue statistical calculation." Theoretical and Applied Fracture Mechanics 59, no. 1 (June 2012): 29–33. http://dx.doi.org/10.1016/j.tafmec.2012.05.004.

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26

Pesinis, Konstantinos, and Kong Fah Tee. "Statistical model and structural reliability analysis for onshore gas transmission pipelines." Engineering Failure Analysis 82 (December 2017): 1–15. http://dx.doi.org/10.1016/j.engfailanal.2017.08.008.

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27

Wang, Shouxiang, Xingyou Zhang, Leijiao Ge, and Lei Wu. "2-D Wind Speed Statistical Model for Reliability Assessment of Microgrid." IEEE Transactions on Sustainable Energy 7, no. 3 (July 2016): 1159–69. http://dx.doi.org/10.1109/tste.2015.2512608.

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28

Wang, Jia, and Zhigang Li. "Degradation model of multi-signals based on a novel feature-selection criterion." Structural Health Monitoring 17, no. 6 (December 10, 2017): 1491–502. http://dx.doi.org/10.1177/1475921717746091.

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Анотація:

In most data-driven prognostics approaches, features extracted from measurements are used in the model to indicate the degradation process and determine the reliability in real time. However, many features with physical meaning commonly exhibit no variation until a failure occurs, which leaves little time to conduct maintenance strategies or replacement policies. Hence, this research presents a novel feature-selection criterion, which enables to select a feature with an obvious trend throughout the entire life, thereby avoiding the problem mentioned. In addition, for reliability estimation and condition monitoring, an innovative model-building method based on identical statistical features extracted from multi-signals is developed, in which the features are considered to be dependent and governed by a copula function. An example is provided to illustrate the application of the proposed two-step methods. The result shows that this method is more convincible and realistic for reliability estimation.

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29

Spanos, Aris. "Akaike-type criteria and the reliability of inference: Model selection versus statistical model specification." Journal of Econometrics 158, no. 2 (October 2010): 204–20. http://dx.doi.org/10.1016/j.jeconom.2010.01.011.

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30

Huang, Jiale, Fei Xiao, and Yang Zhang. "Reliability Evaluation of Pavement Life-Cycle Assessment Model." Modelling and Simulation in Engineering 2018 (October 18, 2018): 1–8. http://dx.doi.org/10.1155/2018/4172519.

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Анотація:

Inventory reliability of the life-cycle assessment (LCA) model highly depends on the data quality and normally exhibits significant uncertainty. A rigorous statistical methodology was established to capture and quantify the inherent uncertainties linked to the results of the LCA model. Two sources of uncertainty, data quality and model, were identified. The former was captured by converting the deterministic value to probability density function using beta distribution according to the evaluation matrix of data quality; the latter was assessed by prescribing variation interval through defining uncertainty factor. The functional equivalent pavement structures were designed, and the corresponding energy consumption and CO2 emission were calculated by the LCA model. A 10% variation was observed for the LCA results and within 30-year analysis span, at the 95% confidence level, and environmental burdens of cement pavement are higher than those of asphalt pavements while the comparison between the two asphalt pavements is not significant statistically. Therefore, the established statistical methodology is capable of capturing the uncertainty of the LCA model and quantifying the reliability the LCA results.

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31

Liu, Hai Bo, Jin Huang Wu, Jun Sheng Wang, and Jian Ting Zheng. "Statistical Analysis of Degradation Data Based on Random Coefficient Distribution." Advanced Materials Research 791-793 (September 2013): 1264–68. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1264.

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Анотація:

In order to analyze the reliability of high reliability and long-life products, the method which is based on the distribution of random coefficient is an effective technical approach. In this paper, first it is analyzed the steps of statistical analysis for degradation data based on the distribution of random coefficient. Then it is discussed in detail the degradation models of random coefficient distribution, the problems of distribution under these models and the advantages and disadvantages of each model.

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32

Álvarez, Miguel Ángel Navas, José Carpio Ibáñez, and Carlos Sancho de Mingo. "Reliability Assessment of Repairable Systems Using Simple Regression Models." International Journal of Mathematical, Engineering and Management Sciences 6, no. 1 (October 29, 2020): 180–92. http://dx.doi.org/10.33889/ijmems.2021.6.1.011.

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Анотація:

The statistical nature of failures in repairable systems does not have a behaviour similar to non-repairable systems. The statistical models developed for the study of the reliability of repairable systems mostly based on the application of stochastic processes. However, there is a group of prediction models for reliability based on time series analysis. Below are the results and conclusions of the application of simple regression models in the escalators Avante model (TNE), in order to assess their potential use by maintenance organizations.

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33

Prasad, Dr R. Satya, S. Murali Mohan, and G. Krishna Mohan. "Exponential two step approach for Time Domain based Software Process Control." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 8, no. 2 (June 20, 2013): 777–86. http://dx.doi.org/10.24297/ijct.v8i2.3385.

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Анотація:

Software Reliability Growth Model is a mathematical model of how the software reliability improves as faults are detected and repaired. In this paper we propose a control mechanism based on the cumulative quantity between observations of time domain failure data using mean value function of Goel-Okumoto model, which is based on Non Homogenous Poisson Process. The model parameters are estimated by a two step approach. Software reliability process can be monitored efficiently by using Statistical Process Control. Control charts are widely used for process monitoring. It assists the software development team to identify failures and actions to be taken during software failure process and hence, assures better software reliability.Â

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34

Zhang, Ke, Yi Bo Zheng, and Hang Yang Yu. "The Study on Electric Power Equipment Reliability Analysis Using Statistical Method." Applied Mechanics and Materials 203 (October 2012): 340–44. http://dx.doi.org/10.4028/www.scientific.net/amm.203.340.

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Анотація:

This paper gives an overview of selected statistical methods and models commonly used in reliability engineering. The purpose of this paper is to discuss several concepts commonly applied in the analysis of equipment performance and, in particular, in predicting the remaining life of power equipment. Parametric methods are well suited for the prediction of the end of life of electric power equipment. However, care must be taken in selecting appropriate model since vastly different results can be obtained with different models fitted into the same data. Nonparametric methods are useful only when no suitable probability distribution can be fitted into the data set.

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35

Lee, Sang Bong, and Youn Mo Lee. "Statistical Reliability Analysis of Numerical Simulation for Prediction of Model-Ship Resistance." Journal of the Society of Naval Architects of Korea 51, no. 4 (August 20, 2014): 321–27. http://dx.doi.org/10.3744/snak.2014.51.4.321.

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36

Alfyorov, О. I. "Statistical model of reliability of cultivators with vibratory movement of working bodies." Naukovij žurnal «Tehnìka ta energetika» 10, no. 1 (February 7, 2019): 5–9. http://dx.doi.org/10.31548/machenergy2019.01.005.

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37

Hund, Lauren, Benjamin Schroeder, Kellin Rumsey, and Gabriel Huerta. "Distinguishing between model- and data-driven inferences for high reliability statistical predictions." Reliability Engineering & System Safety 180 (December 2018): 201–10. http://dx.doi.org/10.1016/j.ress.2018.07.017.

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38

Xie, Chuanning, Xiaoqi Tang, Jihong Chen, Bao Song, Jian Jin, and Hangjun Zhang. "Reliability Analysis and Accelerated Statistical Model of CNC PCB for Electrochemical Migration." IEEE Transactions on Device and Materials Reliability 14, no. 1 (March 2014): 90–98. http://dx.doi.org/10.1109/tdmr.2013.2289297.

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39

Lal, Gend, and Rekha Saha. "A Statistical Approach for Estimating Language Model Reliability with Effective Smoothing Technique." International Journal of Computer Applications 123, no. 16 (August 18, 2015): 31–35. http://dx.doi.org/10.5120/ijca2015905763.

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40

Hanagal, David D., and Nileema N. Bhalerao. "Modeling and Statistical Inference on Generalized Inverse Weibull Software Reliability Growth Model." Journal of the Indian Society for Probability and Statistics 17, no. 2 (July 23, 2016): 145–60. http://dx.doi.org/10.1007/s41096-016-0010-8.

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41

Postnykh, A. M., A. A. Chekalkin, and V. V. Khronusov. "Structural-statistical model of the reliability and durability of a fiber composite." Mechanics of Composite Materials 26, no. 5 (1991): 633–37. http://dx.doi.org/10.1007/bf00616644.

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42

Rastayesh, Sima, Amol Mankar, John Dalsgaard Sørensen, and Sajjad Bahrebar. "Development of Stochastic Fatigue Model of Reinforcement for Reliability of Concrete Structures." Applied Sciences 10, no. 2 (January 14, 2020): 604. http://dx.doi.org/10.3390/app10020604.

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Анотація:

This paper presents recent contributions to the Marie Skłodowska-Curie Innovative Training Network titled INFRASTAR (Innovation and Networking for Fatigue and Reliability Analysis of Structures-Training for Assessment of Risk) in the field of reliability approaches for decision-making for wind turbines and bridges . Stochastic modeling of uncertainties for fatigue strength parameters is an important step as a basis for reliability analyses. In this paper, the Maximum Likelihood Method (MLM) is used for fitting the statistical parameters in a regression model for the fatigue strength of reinforcement bars. Furthermore, application of the Bootstrapping method is investigated. The results indicate that the latter methodology does not work well in the considered case study because of run-out tests within the test data. Moreover, the use of the Bayesian inference with the Markov Chain Monto Carlo approach is studied. These results indicate that a reduction in the statistical uncertainty can be obtained, and thus, better parameter estimates are obtained. The results are used for stochastic modelling in reliability assessment of a case study with a composite bridge. The reduction in statistical uncertainty shows high impact on the fatigue reliability in a case study on the Swiss viaduct Crêt De l’Anneau.

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43

Wang, Jun Sheng, Yi Zhou He, Yi Dong Wang, and Wei Hua Liu. "Statistical Analysis of the Degradation Data Based on Pseudo-Life Distribution." Advanced Materials Research 933 (May 2014): 784–88. http://dx.doi.org/10.4028/www.scientific.net/amr.933.784.

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Focus on solving the life and reliability problems of long-life and high reliability products, a method of modeling and parameter estimation on reliability evaluation and life prediction with accelerated degradation data was proposed in this paper. In the first, three statistical methods of degradation test were analyzed, then according to the features of accelerated degradation data, the statistical model of degradation data based on pseudo-life was established. This method can effectively utilize the horizontal information of degradation data of product under different accelerated stress level, and integrate the advantage that continuous-time function model fits the degradation curve of product strongly, and improve the accuracy of reliability assessment and life prediction of product.

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44

Fahami, Atefeh, and Minoo Motaghi. "The Effect of Pender Model on Elderly Health Promotion Behaviors in Iran." International Journal of Ayurvedic Medicine 10, no. 2 (June 30, 2019): 151–56. http://dx.doi.org/10.47552/ijam.v10i2.1230.

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Background: The statistical indices show rapid aging in Iran’s population. World health organization considers physical activity as the health index in a society. Therefore, one of the best ways to evaluate the health of the elderly is the evaluation of functional status in daily life. Pender health promotion is an appropriate framework to help you diagnose and understand the effective factors on physical activity promotion behaviors. Aim of this study is evaluating the effectiveness of Pender model on elderly health promotion behaviorsreferring towelfare centers. Method: The present study is a quantitative and semi-experimental study. 95 elderly were investigated using multi-stage sampling method in an intervention group and control group.In order to collect data, a physical activity questionnaire and a researcher-made questionnaire "Pender Health Promotion Model for Physical Activity" were used. After confirming the validity of the questionnaire by the experts, in order to determine the reliability, a questionnaire was provided to 50 elderly people and the Cronbach's alpha coefficient 88 was obtained. For statistical analysis, t-test and correlation were used in the software SPSS 16. Result:The results showed that there was no significant difference between the physical activity of the elderly in the experimental and control groups before intervention, but after intervention, physical activity of the experimental group showed a significant increase (p <0.001). Conclusion: According to the results, it is recommended that from all nine proposed constructs that are in the form of a Healthy Phenomenor Model should be used in the educational interventions planning to promote health behaviors, including physical activity.

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Davies, R. B., R. Hales, J. C. Harman, and S. R. Holdsworth. "Statistical Modeling of Creep Rupture Data." Journal of Engineering Materials and Technology 121, no. 3 (July 1, 1999): 264–71. http://dx.doi.org/10.1115/1.2812374.

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A flexible statistical modeling framework for the analysis of creep rupture data is proposed, which offers an improvement on traditional methods of deriving creep rupture strength values and confidence limits. The paper reviews a family of models that can be used to represent the trend relationship between failure times about the trend line, and examines the reliability of extrapolations. Areas of statistical research which would lead to model improvement are discussed, such as variance heterogeneity, left censoring and allowance for the cluster (cast) structure of the data.

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46

Dragan, Irina-Maria, and Alexandru Isaic-Maniu. "An Innovative Model of Reliability—The Pseudo-Entropic Model." Entropy 21, no. 9 (August 30, 2019): 846. http://dx.doi.org/10.3390/e21090846.

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There is an entire field of literature on reliability models. Building reliability models is most frequently done by starting from the failure rate (or hazard rate). Creating a reliability model starts with the specific type of product behavior over time, so there are model classes for the specifics of different product categories: electronic, mechanical products, mixed systems etc. The aim of this paper was to develop a statistical model which would allow the study of the durability of products, and particularly, in the present case, of electrical switches. The procedure has a broad range of applicability and can be extended to whole categories of products that have components both in motion, and therefore subject to wear, and also that bear additional stress. In our case, an electrical switch involves the shock of the electrical contact, which additionally requires the constituent material. This article started from an indicator similar to entropy (an entropy-like transformation) that was developed according to the stated purpose of the paper. We believe that the appropriate name for the proposed indicator is pseudo-entropic transformation, as we operated with the derivative of g(t), which is basically a probability density. The model developed herein is original and, from a practical point of view, it is convenient for treating and developing analytical and practical applications for classes of products subjected, during use, to a process of wear, degradation, and damage by use.

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47

UTKIN, LEV V. "CAUTIOUS RELIABILITY ANALYSIS OF MULTI-STATE AND CONTINUUM-STATE SYSTEMS BASED ON THE IMPRECISE DIRICHLET MODEL." International Journal of Reliability, Quality and Safety Engineering 13, no. 05 (October 2006): 433–53. http://dx.doi.org/10.1142/s0218539306002367.

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Cautious reliability estimates of multi-state and continuum-state systems are studied in the paper under condition that initial data about reliability of components are given in the form of interval-valued observations, measurements or expert judgments. The interval-valued information is processed by means of a set of the imprecise Dirichlet model which can be regarded as a set of Dirichlet distributions. The developed model of reliability provides cautious reliability measures when the number of observations or measurements is rather small. It can be viewed as an extension of models based on random set theory and robust statistical models. A numerical example illustrates the proposed model and an algorithm for computing the system reliability.

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48

Suhir, E. "Statistics-related and reliability-physics-related failure processes in electronics devices and products." Modern Physics Letters B 28, no. 13 (May 30, 2014): 1450105. http://dx.doi.org/10.1142/s021798491450105x.

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The well known and widely used experimental reliability "passport" of a mass manufactured electronic or a photonic product — the bathtub curve — reflects the combined contribution of the statistics-related and reliability-physics (physics-of-failure)-related processes. When time progresses, the first process results in a decreasing failure rate, while the second process associated with the material aging and degradation leads to an increased failure rate. An attempt has been made in this analysis to assess the level of the reliability physics-related aging process from the available bathtub curve (diagram). It is assumed that the products of interest underwent the burn-in testing and therefore the obtained bathtub curve does not contain the infant mortality portion. It has been also assumed that the two random processes in question are statistically independent, and that the failure rate of the physical process can be obtained by deducting the theoretically assessed statistical failure rate from the bathtub curve ordinates. In the carried out numerical example, the Raleigh distribution for the statistical failure rate was used, for the sake of a relatively simple illustration. The developed methodology can be used in reliability physics evaluations, when there is a need to better understand the roles of the statistics-related and reliability-physics-related irreversible random processes in reliability evaluations. The future work should include investigations on how powerful and flexible methods and approaches of the statistical mechanics can be effectively employed, in addition to reliability physics techniques, to model the operational reliability of electronic and photonic products.

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49

Loehnert, Stefan. "About Statistical Analysis of Qualitative Survey Data." International Journal of Quality, Statistics, and Reliability 2010 (July 7, 2010): 1–12. http://dx.doi.org/10.1155/2010/849043.

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Анотація:

Gathered data is frequently not in a numerical form allowing immediate appliance of the quantitative mathematical-statistical methods. In this paper are some basic aspects examining how quantitative-based statistical methodology can be utilized in the analysis of qualitative data sets. The transformation of qualitative data into numeric values is considered as the entrance point to quantitative analysis. Concurrently related publications and impacts of scale transformations are discussed. Subsequently, it is shown how correlation coefficients are usable in conjunction with data aggregation constrains to construct relationship modelling matrices. For illustration, a case study is referenced at which ordinal type ordered qualitative survey answers are allocated to process defining procedures as aggregation levels. Finally options about measuring the adherence of the gathered empirical data to such kind of derived aggregation models are introduced and a statistically based reliability check approach to evaluate the reliability of the chosen model specification is outlined.

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Danquah, Benedikt, Stefan Riedmaier, Yasin Meral, and Markus Lienkamp. "Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning." Applied Sciences 11, no. 5 (February 24, 2021): 1983. http://dx.doi.org/10.3390/app11051983.

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Анотація:

The modelling and simulation process in the automotive domain is transforming. Increasing system complexity and variant diversity, especially in new electric powertrain systems, lead to complex, modular simulations that depend on virtual vehicle development, testing and approval. Consequently, the emerging key requirements for automotive validation involve a precise reliability quantification across a large application domain. Validation is unable to meet these requirements because its results provide little information, uncertainties are neglected, the model reliability cannot be easily extrapolated and the resulting application domain is small. In order to address these insufficiencies, this paper develops a statistical validation framework for dynamic systems with changing parameter configurations, thus enabling a flexible validation of complex total vehicle simulations including powertrain modelling. It uses non-deterministic models to consider input uncertainties, applies uncertainty learning to predict inherent model uncertainties and enables precise reliability quantification of arbitrary system parameter configurations to form a large application domain. The paper explains the framework with real-world data from a prototype electric vehicle on a dynamometer, validates it with additional tests and compares it to conventional validation methods. It is published as an open-source document. With the validation information from the framework and the knowledge deduced from the real-world problem, the paper solves its key requirements and offers recommendations on how to efficiently revise models with the framework’s validation results.

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