A novel time-domain auto-regressive model for structural damage diagnosis

In this paper, a novel time-series model is proposed for the diagnosis of structural damage. Two major issues need be addressed when considering time-domain data for damage detection; one is a damage sensitive feature and the other concerns the fact that the input excitation usually is not measurable. The present approach stems from the linear dynamic system theory and it is formulated in the form of a prediction model of auto-regressive with eXogenous input. With some simplifications, the model is expressed such that only response (acceleration) signals are involved, with response at one location chosen as the “input” of the model. The model coefficients correlate with the dynamic properties of the structure and they can be established from reference-state response signals. The residual error of the established model when applied on actually measured signals reflects the structural change, and the standard deviation of the residual error is found to be a damage sensitive feature. Numerical examples demonstrate that the method can be applied for a rapid detection of structural changes and it can also indicate the damage locations. Furthermore, the model can tolerate certain variation of the actual excitation. The model provides a basis for developing more robust damage sensitive features for real applications.