In reliability theory and survival analysis, observed data are often weakly dependent and subject to additive measurement errors. Such contamination arises when the underlying data are neither independent nor strongly mixed but instead exhibit association. This paper focuses on estimating the hazard rate by deconvolving the density function and constructing an estimator of the distribution function. We assume that the data originate from a strictly stationary sequence satisfying association conditions. Under appropriate smoothness assumptions on the error distribution, we establish the quadratic-mean convergence and asymptotic normality of the proposed estimators. The finite-sample performance of both the hazard rate and distribution function estimators is evaluated through a simulation study. We conclude with a discussion of open problems and potential future research directions.