Mitigation of environmental variabilities in damage detection: A comparative study of two semi-supervised approaches

Vibration-based structural health monitoring (VSHM) employs vibration signals as observables from which inferences are made concerning the integrity of structural systems. More specifically, the premise is to identify damage through changes in a set of features extracted from the vibration signals. A major challenge in this regard is that false positives may arise due to the influence of environmental and operational variabilities (EOVs). Environmental variabilities, e.g. shifts in temperature and humidity, introduce changes in mechanical properties. These changes are reflected in the vibration response and can reduce the probability of detecting damage in a structure.
This paper conducts a comparative study between a novel semi-supervised damage detection approach and a well known cointegration-based scheme to deal with the variabilities induced by EOVs. The novel approach uses the pattern recognition capability of an artificial neural network (ANN) to learn how the EOVs affect a Mahalanobis distance-based damage index in a reference state. The cointegration-based scheme seeks to mitigate EOVs by computing stationary linear combinations of non-stationary output response signals. The merit of the damage detection methods are examined in the context of a mass-spring system, which is exposed to a simulated temperature field that renders output response non-stationary. The system is analysed in a reference state and a perturbed state in which damage is emulated by reducing a single spring stiffness by 2%. Although both approaches successfully mitigate the effects of EOVs, the results show that the ANN-based damage detection approach improved the damage detection accuracy.