  A wide variety of natural and human-made systems consist of a large set of
dynamical units coupled into a complex structure. Breakdown of such systems can
have a dramatic impact, as in the case of neurons in the brain or lines in an
electric grid, to name but a few. Preventing such catastrophic events requires
in particular to be able to detect and locate the source of disturbances as
fast as possible. We propose a simple method to identify and locate
disturbances in networks of coupled dynamical agents, relying only on time
series measurements and on the knowledge of the (possibly Kron-reduced) network
structure. The strength and the appeal of the present approach lies in its
simplicity paired with the ability to precisely locate disturbances and even to
differentiate between line and node disturbances. If we have access to
measurement at only a subset of nodes, our method is still able to identify the
location of the disturbance if the disturbed nodes are measured. If not, we
manage to identify the region of the network where the disturbance occurs.
