CCTV is an expedient tool for monitoring and investigating. Since it can record emergency and routine events in memory of the electronic circuit board. However, in the process of operating CCTV, it is necessary to carry out a sampling inspection on its lifetime to predicate its availability and reliability. According to previous literature, most of the parameter estimation methods used only one parameter estimation method, which is not sufficient for identifying two explicit parameters of Weibull distribution. Moreover, the bad parameter estimation method cannot achieve reliable analysis results. Therefore, this paper presented the four methods of estimation in both graphical and analytical methods for estimating parameters of Weibull distribution. These methods have measured the accuracy by using mean square error (MSE). In addition, the real data set confirmed that there is the Weibull distribution by using the Kolmogorov-Smirnov test. The simulation results found that the maximum likelihood method (MLM) can outperform the other estimator because it can provide a minimum MSE, there is an optimal parameter in which the shape is 4.3881 and the scale is 86.8207. Finally, the two optimal parameters are applied to real data sets of the CCTV. As a result, the optimal parameter fits with real data sets, and it can evaluate the lifespan of CCTV by using the mean time to failure (MTTF) as 6.6 years.

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.