Cointegration: a novel approach for the removal of environmental trends in structural health monitoring data

TLDR

Environmental variability causes non‑stationary dynamic and quasi‑static responses in structures, masking damage‑induced changes and posing a challenge for reliable SHM outside laboratory conditions. The study proposes using cointegration to address environmental variation in SHM data. When monitored variables are cointegrated, a linear combination produces a stationary residual that removes common environmental trends. The resulting stationary residual serves as a damage‑sensitive feature that is independent of normal environmental and operational conditions.

Abstract

Before structural health monitoring (SHM) technologies can be reliably implemented on structures outside laboratory conditions, the problem of environmental variability in monitored features must be first addressed. Structures that are subjected to changing environmental or operational conditions will often exhibit inherently non-stationary dynamic and quasi-static responses, which can mask any changes caused by the occurrence of damage. The current work introduces the concept of cointegration , a tool for the analysis of non-stationary time series, as a promising new approach for dealing with the problem of environmental variation in monitored features. If two or more monitored variables from an SHM system are cointegrated, then some linear combination of them will be a stationary residual purged of the common trends in the original dataset. The stationary residual created from the cointegration procedure can be used as a damage-sensitive feature that is independent of the normal environmental and operational conditions.