Methodology for Designing Diagnostic Data Streams for Use in a Structural Impairment Detection System

Abstract

This paper outlines a general methodology used to design digital data streams of electronic sensors attached to critical components of a structure to be processed by a structural impairment detection system (SIDS). The methodology begins by evaluating a specific structure, establishing a baseline behavior profile, and identifying specific structural impairments that are likely to occur. Finite-element modeling of a specific test bed is presented as a tool with which to design and create diagnostic data streams. Establishing a list probable impairments is critical to designing diagnostic data streams with which to implement a SIDS capable of indicating and, more importantly, identifying digital data signatures that may indicate specific structural impairments. Once a set of impairment scenarios is defined, finite-element models representing those impairments are created and several locations sensitive to modeled impairments are identified. A matrix of different locations and measurement types determines the instrumentation schedule. Through experimental observation and iterative structural analysis, diagnostic data streams are created that serve as (1) patterns for training neural diagnostic algorithms and (2) patterns to be interrogated to evaluate a testbed structure.