Noise can be correlated with itself. For example, if you're using a pressure sensor to detect whether the person carrying it has fallen, then the baseline atmospheric pressure is noise, while the signal is in relatively small changes corresponding to a meter or so of height difference. Because atmospheric pressure changes only slowly, it is highly correlated across successive measurements. If you did PCA on timelines recorded under different atmospheric pressure conditions, that would appear as the top component.

I used that example because something similar actually happened (although there's no indication PCA was used): https://semiengineering.com/training-a-neural-network-to-fal...