Time series predictive models of piezoelectric active-sensing for SHM applications

In this paper, the use of time domain data from piezoelectric active-sensing techniques is investigated for structural health monitoring (SHM) applications. Piezoelectric transducers have been increasingly used in SHM because of their proven advantages. Especially, the use of known and repeatable inputs at high frequency ranges makes the development of SHM signal processing algorithm easier and more efficient. However, to date, most of these techniques have been based on frequency domain analyses, such as impedance-based or high-frequency response functions (FRF) -based SHM techniques. Even with Lamb wave propagations, most researchers adopt frequency domain or wavelets analysis for damage-sensitive feature extraction. This process usually requires excessive averaging to reduce measurement noise and more computational resources, which is not ideal from both memory and power consumption standpoints. Therefore in this study, we investigate the use of autoregressive models with exogenous inputs (ARX) with the measured time series data from piezoelectric active-sensors. The test structure considered in this study is a composite plate, where several damage conditions were manually imposed. The performance of this technique is compared to that of traditional autoregressive models, traditionally used in low-frequency passive sensing SHM applications, and that of FRF-based analysis, and its superior capability for SHM is demonstrated.