In this paper, two novel consistency vectors are proposed, which when combined with appropriate machine learning algorithms, can be used to adapt the Spectral Kurtosis technology for optimum gearbox damage diagnosis in varying operating conditions. Much of the existing research in the field is limited to test apparatus run in constant and carefully controlled operating conditions, and the authors have previously publicised that the Spectral Kurtosis technology requires adaptation to achieve the highest possible probabilities of correct diagnosis when a gearbox is run in non-stationary conditions of speed and load. However, the authors’ previous adaptation has been computationally heavy using a brute-force approach unsuited to online use, and therefore, created the requirement to develop these two newly proposed vectors and allow computationally lighter techniques more suited to online condition monitoring. The new vectors are demonstrated and experimentally validated on vibration data collected from a gearbox run in multiple combinations of operating conditions; for the first time, the two consistency vectors are used to predict diagnosis effectiveness, with the comparison and proof of relative gains between the traditional and novel techniques discussed. Consistency calculations are computationally light and thus, many combinations of Spectral Kurtosis technology parameters can be evaluated on a dataset in a very short time. This study shows that machine learning can predict the total probability of correct diagnosis from the consistency values and this can quickly provide pre-adaptation/prediction of optimum Spectral Kurtosis technology parameters for a dataset. The full adaptation and damage evaluation process, which is computationally heavier, can then be undertaken on a much lower number of combinations of Spectral Kurtosis resolution and threshold.