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Relevant bibliographies by topics / Weibull Analysis

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Journal articles

Academic literature on the topic 'Weibull Analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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Journal articles on the topic "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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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