Symbolic Dynamics-Based Structural Health Monitoring

Abstract

Extraction of robust, damage-sensitive, low-dimensional features lies at the heart of any structural health monitoring (SHM) process. Symbolic dynamics provides a largely unexplored framework for extracting features applicable to multiple damage types and structural platforms. Dynamic systems theory dictates that dynamical systems (continuous-valued in continuous time) have a fully isomorphic (same structure) representation in symbolic dynamics (discrete-time, discrete-alphabet digital streams). Discretization occurs through a process called partitioning, and practical algorithms for partitioning noisy data have been recently developed. The symbolic transformation greatly reduces data-handling requirements while maintaining full data fidelity, and allows the use of sophisticated algorithms from information theory and communications engineering for statistically classifying the features. Thus, utilization of symbolic data and analysis techniques in SHM systems can greatly reduce memory requirements and necessary computational power which can be of great benefit for use in SHM network nodes employing wireless data transfer and energy harvesting technologies. In this preliminary study, a symbolic dynamics-based damage detection algorithm using a partitioning scheme rooted in extreme value statistics is proposed and experimentally validated.