Reliability characteristics analysis of Ready-Mix Cement plant under classical and bayesian inferential framework: A Comparative Analysis - Chaudhary, Ravi; Saini, Monika; Kumar, Ashish; Kumar, Kapil (Brazilian Journal of Biometrics, 2025)
This study introduces a novel stochastic model for assessing the reliability characteristics of a Ready-Mix Cement (RMC) plant. The model employs both classical and Bayesian statistical frameworks. The RMC plant comprises five key components: the rolling belt unit, cement & fly ash storage unit, mixing drum unit, controller unit, and all electric component and motors units. Weibull distribution has been used to simulate failure and repair timeframes, and all time-dependent random variables are treated as statistically independent which allows to assess mean time to system failure, steady-state availability, and busy period. Different scale parameters for each component and a common shape parameter have been used in the model. The investigation is facilitated by the semi-Markov approach and the regeneration point technique, presuming a fully functional repair facility for routine maintenance and repairs. Electronic components and motor have provisions for inspection, but the system is assumed to include provisions for preventative maintenance. Expert repair services are presumed to be instantly available due to component wear and tears and non-repairable electrical elements. To highlight the model’s significance, a Monte Carlo simulation study is conducted, offering a comparative analysis of mean time to system failure (MTSF), availability, and profit functions across traditional, classical, and Bayesian approaches. The results emphasize the effectiveness of the proposed model in optimizing reliability assessments for RMC plants.