A Quantile-Based Approach to Modelling Recovery Time in Structural Health Monitoring

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.