Pairwise graphical models for structural health monitoring with dense sensor arrays. (1st September 2017)

• Record Type: Journal Article
• Authors: Mohammadi Ghazi, Reza; Chen, Justin G.; Büyüköztürk, Oral
• Is Part Of: Mechanical systems and signal processing. Volume 93(2017)
• Page Start: 578
• Page End: 592
• Publication Date: 2017-09-01
• Subjects: Structural health monitoring -- Damage detection -- Graphical models -- Ising model -- Pairwise graphical model -- Sensor network -- Video camera -- Loopy belief propagation -- Gibbs sampling; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2017.02.026
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Pairwise graphical models are used for structural health monitoring (SHM). A mutual information based algorithm is proposed for graph parameter learning. High speed video camera is used to extract the displacement field of a test structure. Loopy belief propagation (LBP) may not be a robust inference method for SHM. MCMC provides the most accurate results compared to LBP and node-wise data analysis. Abstract: Through advances in sensor technology and development of camera-based measurement techniques, it has become affordable to obtain high spatial resolution data from structures. Although measured datasets become more informative by increasing the number of sensors, the spatial dependencies between sensor data are increased at the same time. Therefore, appropriate data analysis techniques are needed to handle the inference problem in presence of these dependencies. In this paper, we propose a novel approach that uses graphical models (GM) for considering the spatial dependencies between sensor measurements in dense sensor networks or arrays to improve damage localization accuracy in structural health monitoring (SHM) application. Because there are always unobserved damaged states in this application, the available information is insufficient for learning the GMs. To overcome this challenge, we propose an approximated model that uses the mutual information between sensor measurements to learn the GMs. The study is backed by experimental validation of the method on two test structures. The first is a three-story two-bay steel model structure that is instrumented by MEMS accelerometers. The second experimental setup consists of a plate structure and a video camera to measure the displacement field of the plate. Our results show that considering the spatial dependencies by the proposed algorithm can significantly improve damage localization accuracy.