Exponentially weighted moving average (EWMA) control charts for time-between-events (TBE) are commonly suggested to monitor high-quality manufacturing processes for the early detection of process deteriorations. In this study, an enhanced one-sided EWMA TBE control chart is developed for rapid detection of increases or decreases in the process mean. The use of the truncation method helps to improve the sensitivity of the proposed scheme in the process mean detection. Moreover, by taking the effects of parameter estimation into account, the proposed scheme with estimated parameters is also investigated in this paper. Both the average run length (ARL) and standard deviation of run length (SDRL) performances of the proposed scheme with known and estimated parameters are studied using the Markov chain method, respectively. Furthermore, an optimal design procedure is developed for the recommended one-sided EWMA TBE chart based on ARL. Numerical results show that the proposed optimal one-sided EWMA TBE chart is more sensitive than the existing optimal one-sided exponential EWMA chart in monitoring both upward and downward mean shifts. Meanwhile, it also performs better than the existing comparative scheme in resisting the effect of the parameter estimation. Finally, two illustrative examples are considered to show the implementation of the recommended one-sided EWMA TBE scheme for simulated and real datasets. For the real dataset of F-16 aircraft, the accident occurrences of F-16 are very low, in which case monitoring the time between two successive F-16 accidents is more efficient than the traditional monitoring schemes. Moreover, the usage of the truncation method makes the proposed one-sided EWMA TBE chart more appropriate for monitoring those two datasets with known shift directions, and the operation of the proposed scheme is simple which does not need to predetermine the reflecting boundary of the existing comparative scheme.