Journal articles: 'Weibull Analysis'

1

Ziegel, EricR. "Weibull Analysis." Technometrics 37, no. 3 (1995): 347–48. http://dx.doi.org/10.1080/00401706.1995.10484347.

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Padgett, W. J. "Weibull Analysis." Journal of Quality Technology 27, no. 4 (1995): 395–96. http://dx.doi.org/10.1080/00224065.1995.11979627.

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Bantan, Rashad A. R., Shakaiba Shafiq, M. H. Tahir, et al. "Statistical Analysis of COVID-19 Data: Using A New Univariate and Bivariate Statistical Model." Journal of Function Spaces 2022 (June 23, 2022): 1–26. http://dx.doi.org/10.1155/2022/2851352.

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In this paper, a new distribution named as unit-power Weibull distribution (UPWD) defined on interval (0,1) is introduced using an appropriate transformation to the positive random variable of the Weibull distribution. This work offers quantile function, linear representation of the density, ordinary and incomplete moments, moment-generating function, probability-weighted moments, L -moments, TL-moments, Rényi entropy, and MLE estimation. Additionally, several actuarial measures are computed. The real data applications are carried out to underline the practical usefulness of the model. In addition, a bivariate extension for the univariate power Weibull distribution named as bivariate unit-power Weibull distribution (BIUPWD) is also configured. To elucidate the bivariate extension, simulation analysis and application using COVID-19-associated fatality rate data from Italy and Belgium to conform a BIUPW distribution with visual depictions are also presented.

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Picoli, S., R. S. Mendes, and L. C. Malacarne. "q-exponential, Weibull, and q-Weibull distributions: an empirical analysis." Physica A: Statistical Mechanics and its Applications 324, no. 3-4 (2003): 678–88. http://dx.doi.org/10.1016/s0378-4371(03)00071-2.

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Gu, Ying Kui, De Liang Ge, and Yao Gang Xiong. "A Reliability Data Analysis Method Using Mixture Weibull Distribution Model." Applied Mechanics and Materials 148-149 (December 2011): 1449–53. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.1449.

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The weibull distribution plays a crucial role in reliability theory and life-testing experiments. Weibull mixtures are widely used to model lifetime and failure time data, since they exhibit a wide range of shapes for the failure rate function. In this paper, the failure data of crank rod system was analyzed by using mixture weibull distribution model. The distribution parameters of the mixture weibull distribution model were estimated by using maximum likelihood estimation and drawing method. The comparison of fitting degree of failure location between standard weibull distribution model and mixture weibull model was given. Results show that the fitting degree of the failure data in the mixture weibull distribution model is higher than that of the simple weibull distribution model, and it can more accurately described the failure distribution curve of the system in life cycle.

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Gu, Ying Kui, and Jing Li. "Engine Failure Data Analysis Method Based on Weibull Distribution Model." Applied Mechanics and Materials 128-129 (October 2011): 850–54. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.850.

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The failure data of crank rod system was analyzed by using weibull parallel model on the base of the simple weibull method. The distribution parameters of the weibull parallel model were estimated by using drawing method. The solving process of WPP drawing method was given in detial. Results show that the fitting degree of the failure data in the weibull parallel model is higher than that of the simple weibull distribution model, and it can more accurately described the failure distribution curve of the system in life cycle, which can provide necessary information for engine reliability indexes computation.

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Crosby, T. M., and G. L. Reinman. "Gas Turbine Safety Improvement Through Risk Analysis." Journal of Engineering for Gas Turbines and Power 110, no. 2 (1988): 265–70. http://dx.doi.org/10.1115/1.3240116.

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This paper is intended to provide the engineer with the information necessary to understand certain statistical methods that are used to improve system safety. It will provide an understanding of Weibull analysis, in that it describes when the Weibull distribution is appropriate, how to construct a Weibull plot, and how to use the parameters of the Weibull distribution to calculate risk. The paper will also provide the engineer with a comprehension of Monte Carlo simulation as it relates to quantifying safety risk. The basic components of Monte Carlo simulation are discussed as well as the formulation of a system model and its application in the gas turbine industry.

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McAndrew, Ian R., Elena Navarro, Orin Godsey, and Brig Gen Usaf. "Drogue System Reliability Analysis Using Weibull Analysis." Applied Mechanics and Materials 798 (October 2015): 622–26. http://dx.doi.org/10.4028/www.scientific.net/amm.798.622.

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Refueling aircraft in-flight is a complex procedure at any time and has its concerns that have not fully been addressed, these are compounded if we consider remote piloting. Long term the need will exist to refuel unmanned vehicles if they are to carry out extensive applications; these complexities of in-flight refueling increases due to time delays and visual challenges between the actual remote aircraft and operators. This paper addresses the reliability of a drogue used in refueling and what can be learnt for future designs and usage. In particular what we can interpret from remote pilots andin-situpilots.

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Badr, Majdah Mohammed, Amal T. Badawi, and Alya S. Alzubidi. "A New Extension of the Exponentiated Weibull Model Mathematical Properties and Modelling." Journal of Function Spaces 2022 (April 28, 2022): 1–10. http://dx.doi.org/10.1155/2022/4669412.

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Recently, several writers have extended the exponentiated Weibull distribution. The five-parameter exponentiated Weibull-exponentiated Weibull (EW-EW) distribution is introduced. In terms of fit, the EW-EW distribution outperforms the EW distribution. Some EW-EW distribution features, such as precise formulas for ordinary moments, quantile, median, and order statistics, are found. Model parameters were estimated using the maximum likelihood technique (ML). The behavior of the various estimators was investigated using a simulated exercise. A medical dataset was utilized to evaluate the practical importance of the EW-EW distribution using additional criteria such as the Akaike information criterion (AKINC), the correct AKINC (COAKINC), the Bayesian INC (BINC), and the Hannan-Quinn INC (HQINC). In terms of performance, we show that the EW-EW distribution beats other models.

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Srisaila, A., D. Rajani, M. V. D. N. S. Madhavi, G. Jaya Lakshmi, K. Amarendra, and Narasimha Rao Dasari. "An Improved Data Generalization Model for Real-Time Data Analysis." Scientific Programming 2022 (August 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/4118371.

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This research proposes a maximum likelihood-Weibull distribution (WD) model for the generalized data distribution family. The distribution function of the anticipated maximum likelihood-Weibull distribution is defined where the statistical properties are derived. The data distribution is capable of modelling monotonically decreasing, increasing, and constant hazard rates. The proposed maximum likelihood-Weibull distribution is used for evaluated these parameters. The experimentation is done to evaluate the potential of the maximum likelihood-Weibull distribution estimated. Here, the online available dataset is adopted for computing the anticipated maximum likelihood-Weibull distribution performance. The outcomes show that the anticipated model is well-suited for computation and compared with other distributions as it possesses maximal and least value of some statistical criteria.

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Qin, Xu, Jiang-she Zhang, and Xiao-dong Yan. "Two Improved Mixture Weibull Models for the Analysis of Wind Speed Data." Journal of Applied Meteorology and Climatology 51, no. 7 (2012): 1321–32. http://dx.doi.org/10.1175/jamc-d-11-0231.1.

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AbstractIn this paper, the authors propose two improved mixture Weibull distribution models by adding one or two location parameters to the existing two-component mixture two-parameter Weibull distribution [MWbl(2, 2)] model. One improved model is the mixture two-parameter Weibull and three-parameter Weibull distribution [MWbl(2, 3)] model. The other improved model is the two-component mixture three-parameter Weibull distribution [MWbl(3, 3)] model. In contrast to existing literature, which has focused on the MWbl(2, 2) and the typical Weibull distribution models, the authors apply the MWbl(2, 3) model and MWbl(3, 3) model to fit the distribution of wind speed data with nearly zero percentages of null wind speed. The parameters of the two improved models are estimated by the maximum likelihood method in which the maximization problem is regarded as a nonlinear programming problem with only inequality constraints and is solved numerically by the interior-point method. The experimental results show that the mixture Weibull models proposed in this paper are more flexible than the existing models for the analysis of wind speed data in practice.

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Loganathan, G. V., C. Y. Kuo, and T. C. McCormick. "Frequency Analysis of Low Flows." Hydrology Research 16, no. 2 (1985): 105–28. http://dx.doi.org/10.2166/nh.1985.0009.

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The transformations (i) SMEMAX (ii) Modified SMEMAX (iii) Power and Probability Distributions (iv) Weibull (α,β,γ) or Extreme value type III (v) Weibull (α,β,0) (vi) Log Pearson Type III (vii) Log Boughton are considered for the low flow analysis. Also, different parameter estimating procedures are considered. Both the Weibull and log Pearson can have positive lower bounds and thus their use in fitting low flow probabilities may not be physically justifiable. A new derivation generalizing the SMEMAX transformation is proposed. A new estimator for the log Boughton distribution is presented. It is found that the Boughton distribution with Cunnane's plotting position provides a good fit to low flows for Virginia streams.

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Alshenawy, R. "The Generalization Inverse Weibull Distribution Related to X-Gamma Generator Family: Simulation and Application for Breast Cancer." Journal of Function Spaces 2022 (June 3, 2022): 1–17. http://dx.doi.org/10.1155/2022/4693490.

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The aim of this paper is to propose the new three-parameter X-Gamma inverse Weibull (XGAIW) distribution which generalizes the inverse Weibull model. The density function of the XGAIW can be expressed as a linear combination of the inverse Weibull densities. Some mathematical quantities (reliability and hazard rate properties) of the proposed XGAIW model are derived. Moreover, four estimation methods, namely, the maximum likelihood, maximum product spacing, least squares, and weighted least squares methods, are utilized to estimate the XGAIW parameters. The Monte Carlo simulation study has been performed to assess the performance of the proposed estimation methods using some criteria. The importance, flexibility, and potentiality of the XGAIW model are studied via a breast cancer data set application. The XGAIW model can produce better fits than some well-known distributions, so the proposed model can be used, as a good alternative to some existing distributions, in modeling several real data.

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Jones, MC, Angela Noufaily, and Kevin Burke. "A bivariate power generalized Weibull distribution: A flexible parametric model for survival analysis." Statistical Methods in Medical Research 29, no. 8 (2019): 2295–306. http://dx.doi.org/10.1177/0962280219890893.

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We are concerned with the flexible parametric analysis of bivariate survival data. Elsewhere, we argued in favour of an adapted form of the ‘power generalized Weibull’ distribution as an attractive vehicle for univariate parametric survival analysis. Here, we additionally observe a frailty relationship between a power generalized Weibull distribution with one value of the parameter which controls distributional choice within the family and a power generalized Weibull distribution with a smaller value of that parameter. We exploit this relationship to propose a bivariate shared frailty model with power generalized Weibull marginal distributions linked by the BB9 or ‘power variance function’ copula, then change it to have adapted power generalized Weibull marginals in the obvious way. The particular choice of copula is, therefore, natural in the current context, and the corresponding bivariate adapted power generalized Weibull model a novel combination of pre-existing components. We provide a number of theoretical properties of the models. We also show the potential of the bivariate adapted power generalized Weibull model for practical work via an illustrative example involving a well-known retinopathy dataset, for which the analysis proves to be straightforward to implement and informative in its outcomes.

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15

August, R., and E. V. Zaretsky. "Incorporating Finite Element Analysis into Component Life and Reliability." Journal of Mechanical Design 115, no. 4 (1993): 706–10. http://dx.doi.org/10.1115/1.2919258.

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A method for estimating a component’s design survivability by incorporating finite element analysis and probabilistic material properties was developed. The method evaluates design parameters through direct comparisons of component survivability expressed in terms of Weibull parameters. The analysis was applied to a rotating disk with mounting bolt holes. The highest probability of failure occurred at, or near, the maximum shear stress region of the bolt holes. Distribution of material failure as a function of Weibull slope affects the probability of survival. Where Weibull parameters are unknown for a rotating disk, it may be permissible to assume Weibull parameters, as well as the stress-life exponent, in order to determine the qualitative effect of disk speed on the probability of survival.

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Benaicha, H., and A. Chaker. "Weibull Mixture Model for Reliability Analysis." International Review of Electrical Engineering (IREE) 9, no. 5 (2014): 986. http://dx.doi.org/10.15866/iree.v9i5.4021.

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17

Mudholkar, Govind S., and Georgia D. Kollia. "Generalized weibull family: a structural analysis." Communications in Statistics - Theory and Methods 23, no. 4 (1994): 1149–71. http://dx.doi.org/10.1080/03610929408831309.

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Borba, M., A. Della Bona, P. F. Cesar, et al. "Weibull analysis of three ceramic materials." Dental Materials 25, no. 5 (2009): e6. http://dx.doi.org/10.1016/j.dental.2009.01.010.

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19

Balakrishnan, N. "The Weibull Analysis Handbook, Second Edition." Journal of Quality Technology 39, no. 1 (2007): 85–86. http://dx.doi.org/10.1080/00224065.2007.11917675.

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20

Olsson, Karl-Erik. "Weibull analysis of fatigue test data." Quality and Reliability Engineering International 10, no. 5 (1994): 437–38. http://dx.doi.org/10.1002/qre.4680100511.

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Łysiak, Grzegorz. "Fracture toughness of pea: Weibull analysis." Journal of Food Engineering 83, no. 3 (2007): 436–43. http://dx.doi.org/10.1016/j.jfoodeng.2007.03.034.

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22

Hurtler, Gisela. "Graphical weibull analysis of repairable systems." Quality and Reliability Engineering International 1, no. 1 (1985): 23–26. http://dx.doi.org/10.1002/qre.4680010106.

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23

Møltoft, J. "Statistical Analysis of Data From Electronic Component Lifetests (A Tutorial Paper)." Active and Passive Electronic Components 12, no. 4 (1987): 259–79. http://dx.doi.org/10.1155/1987/23687.

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Methods of statistically analysing data from electronic component lifetests are discussed. Particular emphasis is given to analysis techniques using the assumptions of constant hazard rate (Exponential distribution), the Weibull distribution and mixed Weibull distributions. The methods used for analysing Weibull data when the data itself is non-uniform due to both removal of test samples during test and also the non-continuance of surveillance of components under test are discussed. Attention is finally given to the effect of two or more failure mechanisms which can produce S-shaped patterns when data is plotted on Weibull Graph paper.Numerous examples are given, mainly from the field of analysis of CMOS circuit components.

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Jiang, Dan, Cong Ling Wang, and Ping Yang. "Reliability Analysis and Goodness-of-Fit Test of Pen Cylinder." Key Engineering Materials 522 (August 2012): 578–81. http://dx.doi.org/10.4028/www.scientific.net/kem.522.578.

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According to the durability test results of pen cylinders, including failure modes and fatigue life data, life distribution of pen cylinder is studied. The parameters of Weibull distribution of pen cylinder are estimated by least square method. Using Weibull distribution test, the goodness-of-fit test of the pen cylinder life distribution is carried out. Results of goodness-of-fit test show fatigue life distribution of pen cylinder complies with Weibull distribution with two parameters.

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Sia, C. V., J. S. Y. Wong, S. K. Thangavelu, K. H. Chong, and A. Joseph. "Weibull Strength Analysis of Pineapple Leaf Fiber." Materials Science Forum 1030 (May 2021): 45–52. http://dx.doi.org/10.4028/www.scientific.net/msf.1030.45.

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Pineapple leave fiber (PALF) can be considered as one of the green materials to the industries, which is the potential to replace the non-renewable synthetic fiber. However, the high disparity in the mechanical properties of PALF becomes an issue in structural composite design. Hence, improved Weibull distribution is utilised to quantify the tensile strength variation of PALF in various gauge lengths. The single fiber tensile test was performed after the fiber surface treatment and fiber diameter scanning. The predicted PALF strength by applying the improved Weibull distribution incorporating with conical frustum model is well compromised with experimental data compared to the traditional Weibull model.

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Gaber, Abdelrahman H., Mahmoud H. Ismail, and Hebat-Allah M. Mourad. "Outage Probability Analysis of Cooperative Diversity Networks Over Weibull and Weibull-Lognormal Channels." Wireless Personal Communications 70, no. 2 (2012): 695–708. http://dx.doi.org/10.1007/s11277-012-0715-2.

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Tiryakioğlu, Murat. "Weibull Analysis of Mechanical Data for Castings II: Weibull Mixtures and Their Interpretation." Metallurgical and Materials Transactions A 46, no. 1 (2014): 270–80. http://dx.doi.org/10.1007/s11661-014-2610-9.

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Alomani, Ghadah, and Mohamed Kayid. "Characterizations of Lifetime Distributions Using Two Relative Reliability Measures." Journal of Function Spaces 2022 (September 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/6476030.

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In this paper, a general characterization property considering two new dynamic relative reliability measures is obtained. The new dynamic relative reliability measures are expressed as the ratio of hazard rates and as the ratio of reversed hazard rates. The measures are evaluated partially at some sequential random times following a specific distribution. We show that several particular statistics, as random times, fulfill that specific distribution, and thus, the result is applicable in the context of the specified random times. The results are applied to some examples to characterize the Weibull distribution and the inverse Weibull distribution.

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Abouelmagd, T. H. M., Mohammed S. Hamed, and Haitham M. Yousof. "Poisson Burr X Weibull distribution." Journal of Nonlinear Sciences and Applications 12, no. 03 (2018): 173–83. http://dx.doi.org/10.22436/jnsa.012.03.05.

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Abouelmagd, T. H. M., Mohammed S. Hamed, Jehan A. Almamy Almamy, M. Masoom Ali, Haitham M. Yousof, and Mustafa C. Korkmaz. "Extended Weibull log-logistic distribution." Journal of Nonlinear Sciences and Applications 12, no. 08 (2019): 523–34. http://dx.doi.org/10.22436/jnsa.012.08.03.

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Holland, F. A., and E. V. Zaretsky. "Investigation of Weibull Statistics in Fracture Analysis of Cast Aluminum." Journal of Mechanical Design 112, no. 2 (1990): 246–54. http://dx.doi.org/10.1115/1.2912599.

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The fracture strengths of two large batches of A357-T6 cast aluminum coupon specimens were compared by using two-parameter Weibull analysis. The minimum number of these specimens necessary to find the fracture strength of the material was determined. The applicability of three-parameter Weibull analysis was also investigated. A design methodology based on the combination of elementary stress analysis and Weibull statistical analysis is advanced and applied to the design of a spherical pressure vessel shell. The results from this design methodology are compared with results from the applicable ASME pressure vessel code.

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Feng, Zhen Yu, Ke Yi Mao, and Tian Chun Zou. "Comparison of Conservatism on Applying Statistical Analysis Method to Determine Composite Allowance." Applied Mechanics and Materials 117-119 (October 2011): 930–35. http://dx.doi.org/10.4028/www.scientific.net/amm.117-119.930.

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In this paper, composite static strength test are analyzed with the Weibull and normal statistical method. Compare the Weibull analysis with the normal analysis, which shows that the A-basis value and B-basis value from the Weibull analysis respectively are less than ones from normal analysis by approximate 9.7% and 2.6% under tension loading, on the other hand they are decreased by approximate 16.1% and 5.2% under compression loading. This analysis result has the higher application worth.

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Shoaib, Muhammad, Saif Ur Rehman, Imran Siddiqui, Shafiqur Rehman, Shamim Khan, and Zia Ibrahim. "Comparison of Weibull and Gaussian Mixture Models for Wind Speed Data Analysis." International Journal of Economic and Environmental Geology 11, no. 1 (2020): 10–16. http://dx.doi.org/10.46660/ijeeg.vol11.iss1.2020.405.

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In order to have a reliable estimate of wind energy potential of a site, high frequency wind speed and direction data recorded for an extended period of time is required. Weibull distribution function is commonly used to approximate the recorded data distribution for estimation of wind energy. In the present study a comparison of Weibull function and Gaussian mixture model (GMM) as theoretical functions are used. The data set used for the study consists of hourly wind speeds and wind directions of 54 years duration recorded at Ijmuiden wind site located in north of Holland. The entire hourly data set of 54 years is reduced to 12 sets of hourly averaged data corresponding to 12 months. Authenticity of data is assessed by computing descriptive statistics on the entire data set without average and on monthly 12 data sets. Additionally, descriptive statistics show that wind speeds are positively skewed and most of the wind data points are observed to be blowing in south-west direction. Cumulative distribution and probability density function for all data sets are determined for both Weibull function and GMM. Wind power densities on monthly as well as for the entire set are determined from both models using probability density functions of Weibull function and GMM. In order to assess the goodness-of-fit of the fitted Weibull function and GMM, coefficient of determination (R2) and Kolmogorov-Smirnov (K-S) tests are also determined. Although R2 test values for Weibull function are much closer to ‘1’ compared to its values for GMM. Nevertheless, overall performance of GMM is superior to Weibull function in terms of estimated wind power densities using GMM which are in good agreement with the power densities estimated using wind data for the same duration. It is reported that wind power densities for the entire wind data set are 307 W/m2 and 403.96 W/m2 estimated using GMM and Weibull function, respectively.

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Shoaib, Muhammad, Saif Ur Rehman, Imran Siddiqui, Shafiqur Rehman, Shamim Khan, and Zia Ibrahim. "Comparison of Weibull and Gaussian Mixture Models for Wind Speed Data Analysis." International Journal of Economic and Environmental Geology 11, no. 1 (2020): 10–16. http://dx.doi.org/10.46660/ojs.v11i1.405.

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In order to have a reliable estimate of wind energy potential of a site, high frequency wind speed and direction data recorded for an extended period of time is required. Weibull distribution function is commonly used to approximate the recorded data distribution for estimation of wind energy. In the present study a comparison of Weibull function and Gaussian mixture model (GMM) as theoretical functions are used. The data set used for the study consists of hourly wind speeds and wind directions of 54 years duration recorded at Ijmuiden wind site located in north of Holland. The entire hourly data set of 54 years is reduced to 12 sets of hourly averaged data corresponding to 12 months. Authenticity of data is assessed by computing descriptive statistics on the entire data set without average and on monthly 12 data sets. Additionally, descriptive statistics show that wind speeds are positively skewed and most of the wind data points are observed to be blowing in south-west direction. Cumulative distribution and probability density function for all data sets are determined for both Weibull function and GMM. Wind power densities on monthly as well as for the entire set are determined from both models using probability density functions of Weibull function and GMM. In order to assess the goodness-of-fit of the fitted Weibull function and GMM, coefficient of determination (R2) and Kolmogorov-Smirnov (K-S) tests are also determined. Although R2 test values for Weibull function are much closer to ‘1’ compared to its values for GMM. Nevertheless, overall performance of GMM is superior to Weibull function in terms of estimated wind power densities using GMM which are in good agreement with the power densities estimated using wind data for the same duration. It is reported that wind power densities for the entire wind data set are 307 W/m2 and 403.96 W/m2 estimated using GMM and Weibull function, respectively.

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Ashraf-Ul-Alam, Md, and Athar Ali Khan. "Generalized Topp-Leone-Weibull AFT Modelling: A Bayesian Analysis with MCMC Tools using R and Stan." Austrian Journal of Statistics 50, no. 5 (2021): 52–76. http://dx.doi.org/10.17713/ajs.v50i5.1166.

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The generalized Topp-Leone-Weibull (GTL-W) distribution is a generalization of Weibull distribution which is obtained by using generalized Topp-Leone (GTL) distribution as a generator and considering Weibull distribution as a baseline distribution. Weibull distribution is a widely used survival model that has monotone- increasing or decreasing hazard. But it cannot accommodate bathtub shaped and unimodal shaped hazards. As a survival model, GTL-W distribution is more flexible than the Weibull distribution to accommodate different types of hazards. The present study aims at fitting GTL-W model as an accelerated failure time (AFT) model to censored survival data under Bayesian setting using R and Stan languages. The GTL-W AFT model is compared with its sub-model and the baseline model. The Bayesian model selection criteria LOOIC and WAIC are applied to select the best model.

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Yeh, Hsiaw-Chan. "Multivariate semi-Weibull distributions." Journal of Multivariate Analysis 100, no. 8 (2009): 1634–44. http://dx.doi.org/10.1016/j.jmva.2009.01.015.

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Pan, Zhi Yuan, and Ming Yan. "Experimental Investigation and Stress Analysis on Static Fatigue Property of Optical Fiber." Advanced Materials Research 531 (June 2012): 642–46. http://dx.doi.org/10.4028/www.scientific.net/amr.531.642.

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This paper mainly presents experimental investigation and stress analysis on static fatigue property of optical fiber. By analyzing the weibull distribution of experiment, the weibull modulus was obtained. From the weibull distribution, the modulus decreased continuously with the decrease of stress, and a conclusion that experiment distributes discretely can also be drawn. Simultaneously, based on the experiment, we also obtain the feature life of optical fiber in different stress and s-t curve of the static fatigue experiment, as well as the analytical expression of static fatigue .

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Shao, Jiaxing, Fang Wang, Lu Li, and Junqian Zhang. "Scaling Analysis of the Tensile Strength of Bamboo Fibers Using Weibull Statistics." Advances in Materials Science and Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/167823.

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This study demonstrates the effect of weak-link scaling on the tensile strength of bamboo fibers. The proposed model considers the random nature of fiber strength, which is reflected by using a two-parameter Weibull distribution function. Tension tests were performed on samples that could be scaled in length. The size effects in fiber length on the strength were analyzed based on Weibull statistics. The results verify the use of Weibull parameters from specimen testing for predicting the strength distributions of fibers of longer gauge lengths.

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Jin, Ting, Zihang Wang, Qiwei Wang, Dasheng Wang, Yuebing Li, and Mingjue Zhou. "Weibull stress analysis for the corner crack in reactor pressure vessel nozzle." Advances in Mechanical Engineering 11, no. 12 (2019): 168781401989356. http://dx.doi.org/10.1177/1687814019893567.

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The nozzle region of a reactor pressure vessel experiences higher and more complex stresses than the remaining part of the reactor pressure vessel. If a corner crack is postulated in the nozzle region, it is necessary to consider the potential strong influence of constraint on fracture behavior due to inelastic deformations of crack tip. In accordance with the requirement of probabilistic fracture mechanics, Weibull stress in local approach to fracture is analyzed with the consideration of constraint effect. Conventional fracture analysis is also carried out using a three-dimensional crack model, and the fracture driving forces ( KI and J) and T-stress are obtained. Weibull stress along the crack tip is also computed by three-dimensional models. The modified boundary layer model with plastic correction is developed for a corner crack in the reactor pressure vessel nozzle. Under the J- T stress field from three-dimensional models, Weibull stress values obtained using the modified boundary layer model are compared and discussed with that by three-dimensional models. It is found that the modified boundary layer model can effectively predict the Weibull stress under the J- T stress field, which simplified the Weibull stress calculation process for complex structures.

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Kayid, Mohamed, and Salah Djemili. "Reliability Analysis of the Inverse Modified Weibull Model with Applications." Mathematical Problems in Engineering 2022 (April 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/4005896.

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Abstract:

This paper presents a novel flexible inverse modified Weibull model with a concave Weibull probability diagram that describes various reliability phenomena. We investigate some essential attributes and reliability properties of this model. The new model exhibits an increasing or unimodal form of the hazard rate function, making it more suitable for simulating certain aging classes of life distributions with appropriate choices of parameter values. We used the Weibull probability paper approach and the maximum likelihood method to estimate the model's parameters. In addition, we conduct a simulation study to investigate the behavior and efficiency of these estimators. Finally, we analyze a maintenance dataset to demonstrate the usefulness of the proposed model.

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Chen, Wen-hua, Jie Cui, Xiao-yan Fan, Xian-biao Lu, and Ping Xiang. "Reliability analysis of DOOF for weibull distribution." Journal of Zhejiang University-SCIENCE A 4, no. 4 (2003): 448–53. http://dx.doi.org/10.1631/jzus.2003.0448.

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Venkatesan, G., and P. Saranya. "Weibull Survival Model and Its Posterior Analysis." International Journal of Scientific Research in Mathematical and Statistical Sciences 5, no. 5 (2018): 121–37. http://dx.doi.org/10.26438/ijsrmss/v5i5.121137.

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Rifkin, R. "Analysis of CFAR performance in Weibull clutter." IEEE Transactions on Aerospace and Electronic Systems 30, no. 2 (1994): 315–29. http://dx.doi.org/10.1109/7.272257.

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Chaturvedi, Anoop, Manaswini Pati, and Sanjeev K. Tomer. "Robust Bayesian analysis of Weibull failure model." METRON 72, no. 1 (2013): 77–95. http://dx.doi.org/10.1007/s40300-013-0027-7.

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Amaral, P. M., J. Cruz Fernandes, and L. Guerra Rosa. "Weibull statistical analysis of granite bending strength." Rock Mechanics and Rock Engineering 41, no. 6 (2007): 917–28. http://dx.doi.org/10.1007/s00603-007-0154-7.

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Tang, Y., M. Xie, and T. N. Goh. "Statistical Analysis of a Weibull Extension Model." Communications in Statistics - Theory and Methods 32, no. 5 (2003): 913–28. http://dx.doi.org/10.1081/sta-120019952.

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Zhang, Tieling, and Min Xie. "Failure Data Analysis with Extended Weibull Distribution." Communications in Statistics - Simulation and Computation 36, no. 3 (2007): 579–92. http://dx.doi.org/10.1080/03610910701236081.

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You, Minglei, Hongjian Sun, Jing Jiang, and Jiayi Zhang. "Effective Rate Analysis in Weibull Fading Channels." IEEE Wireless Communications Letters 5, no. 4 (2016): 340–43. http://dx.doi.org/10.1109/lwc.2016.2558179.

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Berger, James O., and Dongchu Sun. "Bayesian Analysis for the Poly-Weibull Distribution." Journal of the American Statistical Association 88, no. 424 (1993): 1412–18. http://dx.doi.org/10.1080/01621459.1993.10476426.

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Aarseth, K. A., and E. Prestløkken. "Mechanical Properties of Feed Pellets: Weibull Analysis." Biosystems Engineering 84, no. 3 (2003): 349–61. http://dx.doi.org/10.1016/s1537-5110(02)00264-7.

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Journal articles on the topic 'Weibull Analysis'

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