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Subject: search.filters.subject.Weibull distribution

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    Lifespan Estimation of Closed-Circuit Television (CCTV) Systems Using Weibull Modeling
    (2026-02-01)
    Chutchavong, Vanvisa
    ;
    Pirajnanchai, Virote
    ;
    Doungpan, Satawat
    ;
    Kasettakarn, Witchapon
    ;
    Phungtua-eng, Thanapol
    Ensuring the availability and reliability of electronic components or materials during operation necessitates conducting a sampling inspection of their lifespan. This is particularly crucial when comparing equipment or materials deployed in various areas with diverse environments, conditions, and situations. Many studies rely on one-method parameter estimation methods, which fall short in identifying the two explicit parameters of the Weibull distribution. Moreover, inaccurate parameter estimation methods impede the attainment of reliable analysis results. Consequently, this paper introduces three analytical estimation methods to determine the parameters of the Weibull distribution. The accuracy of these methods is evaluated using the mean square error (MSE). Furthermore, we utlized the Kolmogorov–Smirnov and Anderson–Darling test on real datasets, confirming that the data follow a Weibull distribution. Simulation results indicate that the maximum likelihood estimate outperforms the other estimators by minimizing the MSE and yielding optimal parameters. These optimal parameters were then applied to real CCTV datasets, demonstrating a good fit and enabling assessment of CCTV lifespan through the mean time to failure, which is estimated to be 6.6 years. This holds true even when the equipment operates in environments with different conditions and situations.
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    Evaluation of wind energy production using weibull distribution and artificial neural networks
    (2018-08-13)
    Wannakam, Khanittha
    ;
    Jiriwibhakorn, Somchat
    Wind turbine power generation planning requires production estimation. Wind power is uncertain depending on the location, wind speed and wind turbine efficiency. This paper presents a method for evaluating wind energy production using Weibull distribution and Artificial neural networks to compare the data recorded by Promthep Alternative Energy Station, Phuket, Thailand. The results show that wind energy estimation using artificial neural networks produces the most accurate results. Mean Absolute Percentage Error is used to determine the minimum error value. Minimal error of training data is 2.524% and the test data is 3.3041%.
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    Assessment of wind power generation
    (2018-08-13)
    Wannakam, Khanittha
    ;
    Jiriwibhakorn, Somchat
    Wind power generation is an unstable source of renewable energy. Electricity depends on the weather at the installation location of the wind turbine. This paper presents the prediction of wind power by using the Weibull distribution and Adaptive Neuro-Fuzzy Inference System. The lowest error rates from the Adaptive Neuro-Fuzzy Inference System were 2.1912% and 4.5678%. This is the error value of the training data and the test data respectively.