<![CDATA[This paper introduces a hagoslot stochastic model to investigate the failure times of bearings based on a two-parameter Weibull distribution utilizing Monte Carlo simulation. The failure times were fitted based on maximum likelihood estimation, and the parameters showed that the wear-out failure type with an increasing hazard rate (β>1) was corresponding to the fatigue-induced breakdown phenomenon in the rolling bearings. A Monte Carlo simulation with 1000 runs was performed to quantify the uncertainty of lifetime predictions, which have presented relatively high spreads but stable central tendencies in the Weibull parameter estimates. Survival analysis and hazard function showed increasing probability of failure with time, indicative of the need for prognosis-based maintenance. The findings demonstrate that the Weibull model is a reliable and interpretable paradigm that can be used to describe the probabilistic nature of mechanical component failure. The presented modeling strategy is appropriate for both engineering purposes and simulation-based reliability analyses, possibly evolved into a mixture-Weibull representation or data-driven parameter estimation.]]>
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