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The frequency-domain approach to fatigue life estimation in random loading has been largely investigated due to its computational advantages, and several methods for the frequency translation of most common time-domain methods have been proposed. However, the largest part of frequency methods only focuses on the estimation of the marginal amplitude probability density function of cycles and do not consider the damage increment caused by the positive mean value of the counted cycles. This paper aims to deepen the effect of the random mean value of the cycles on fatigue damage estimation by simulating a large number of time histories with different power spectral densities and computing the resulting damage for a steel material using time-domain instruments like rainflow count, Goodman correction for mean value and Palmgren-Miner linear rule for damage cumulation. The influence of the presence of a non-zero global mean value of the process on the damage computation due to the random cycle mean value has been investigated. The study of the effect of signal PSD resulted in a non-monotonic influence on the ratio between the total damage and an approximated damage that does not consider random mean values. Finally, some further simulations have been done to study the effect of variations in the S-N curve of the material.