Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering
Abbreviated Journal Title
J. Sound Vibr.
STATISTICAL PATTERN-RECOGNITION; FAULT-DETECTION; IDENTIFICATION; DIAGNOSIS; ALGORITHM; Acoustics; Engineering, Mechanical; Mechanics
This study presents a novel time series analysis methodology to detect, locate, and estimate the extent of the structural changes (e.g. damage). In this methodology, ARX models (Auto-Regressive models with exogenous input) are created for different sensor clusters by using the free response of the structure. The output of each sensor in a cluster is used as an input to the ARX model to predict the output of the reference channel of that sensor cluster. Two different approaches are used for extracting Damage Features (DFs) from these ARX models. For the first approach, the coefficients of the ARX models are directly used as the DFs. It is shown with a 4 dof numerical model that damage can be identified, located and quantified for simple models and noise free data. To consider the effects of the noise and model complexity, a second approach is presented based on using the ARX model fit ratios as the DFs. The second approach is first applied to the same 4 DOF numerical model and to the numerical data coming from an international benchmark study for noisy conditions. Then, the methodology is applied to the experimental data from a large scale laboratory model. It is shown that the second approach performs successfully for different damage cases to identify and locate the damage using numerical and experimental data. Furthermore, it is observed that the OF level is a good indicator for estimating the extent of the damage for these cases. The potential and advantages of the methodology are discussed along with the analysis results. The limitations of the methodology, recommendations, and future work are also addressed. (C) 2010 Elsevier Ltd. All rights reserved.
Journal of Sound and Vibration
"Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering" (2011). Faculty Bibliography 2010s. 1336.