We apply interval-based timing constraint satisfaction probability results to predict timing constraint violations in real-time embedded system with a known hardware transient failure model. A previous study indicated that hardware transient failures follow a Poisson distribution with an average failure arrival rate lambda. Under this model, the distribution of time intervals between successive failures follows an exponential distribution with the same parameter lambda. Our goal is to use the statistical transient failure models to calculate the earliest time at which we can predict, with a determined level of confidence, that a given timing constraint may be violated. This earlier prediction provides time-critical systems with valuable time before the deadline is reached to adapt themselves, and hence, to minimize possible negative impacts caused by timing constraint violations