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arXiv · 1803.08444

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

Alastair Gregory·F. Din-Houn Lau·Liam Butler
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Abstract

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.

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Alastair Gregory, F. Din-Houn Lau, Liam Butler. 2018-03-22. A Quantile-Based Approach to Modelling Recovery Time in Structural Health Monitoring. https://arxiv.org/abs/1803.08444

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