Most statistics for fault detection in engineering systems are designed to detect shifts in the process distribution parameters, such as mean or variance shifts. The incipient fault is, however, more likely to affect the probability distributions in an unpredictable (random) manner. It causes slight distortions along the distribution shape rather than a particular parametric change. In this paper, it will be shown that the detection of such a fault requires rather nonparametric informational distances, which measure the dissimilarity between two probability distributions. In particular, The Kullback-Leibler (KL) divergence is proposed to be a fault indicator. Once it has been applied to eddy-currents testing (ECT) signals, it is shown able to reveal the signature of minor cracks (0.01 mm 2 ). The KL divergence, viewed as a global fault indicator, will be compared to the local statistical moments. It is going to show a higher sensitivity to the imperceptible changes, which are caused by minor cracks.