Structural damage identification using piezoelectric impedance measurement with sparse inverse analysis. (21st February 2018)

• Record Type: Journal Article
• Authors: Cao, Pei; Qi, Shuai; Tang, J
• Is Part Of: Smart materials and structures. Volume 27:Number 3(2018:Mar.)
• Publication Date: 2018-02-21
• Subjects: damage identification -- piezoelectric impedance/admittance -- inverse analysis -- sparse regularization -- multi-objective optimization -- DIRECT algorithm -- l0 norm; Smart materials -- Periodicals; Strucural design -- Periodicals; 620.11
• DOI: 10.1088/1361-665X/aaacba
• Languages: English
• ISSNs: 0964-1726

Abstract

Abstract: The impedance/admittance measurements of a piezoelectric transducer bonded to or embedded in a host structure can be used as damage indicator. When a credible model of the healthy structure, such as the finite element model, is available, using the impedance/admittance change information as input, it is possible to identify both the location and severity of damage. The inverse analysis, however, may be under-determined as the number of unknowns in high-frequency analysis is usually large while available input information is limited. The fundamental challenge thus is how to find a small set of solutions that cover the true damage scenario. In this research we cast the damage identification problem into a multi-objective optimization framework to tackle this challenge. With damage locations and severities as unknown variables, one of the objective functions is the difference between impedance-based model prediction in the parametric space and the actual measurements. Considering that damage occurrence generally affects only a small number of elements, we choose the sparsity of the unknown variables as another objective function, deliberately, the l 0 norm. Subsequently, a multi-objective Dividing RECTangles (DIRECT) algorithm is developed to facilitate the inverse analysis where the sparsity is further emphasized by sigmoid transformation. As a deterministic technique, this approach yields results that are repeatable and conclusive. In addition, only one algorithmic parameter, the number of function evaluations, is needed. Numerical and experimental case studies demonstrate that the proposed framework is capable of obtaining high-quality damage identification solutions with limited measurement information.