  Statistical techniques play a large role in the structural health monitoring
of instrumented infrastructure, such as a railway bridge constructed with an
integrated network of fibre optic sensors. One possible way to reason about the
structural health of such a railway bridge, is to model the time it takes to
recover to a no-load (baseline) state after a train passes over. Inherently,
this recovery time is random and should be modelled statistically. This paper
uses a non-parametric model, based on empirical quantile approximations, to
construct a space-memory efficient baseline distribution for the streaming data
from these sensors. A fast statistical test is implemented to detect deviations
away from, and recovery back to, this distribution when trains pass over the
bridge, yielding a recovery time. Our method assumes that there are no temporal
variations in the data. A median-based detrending scheme is used to remove the
temporal variations likely due to temperature changes. This allows for the
continuous recording of sensor data with a space-memory constraint.
