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While piezoelectric impedance/admittance measurements have been used for fault detection and identification, the actual identification of fault location and severity remains to be a challenging topic. On one hand, the approach that uses these measurements entertains high detection sensitivity owing to the high-frequency actuation/sensing nature. On the other hand, high-frequency analysis requires high dimensionality in the model and the subsequent inverse analysis contains a very large number of unknowns which often renders the identification problem under-determined. A new fault identification algorithm is developed in this research for piezoelectric impedance/admittance based measurement. Taking advantage of the algebraic relation between the sensitivity matrix and the admittance change measurement, we devise a pre-screening scheme that can rank the likelihoods of fault locations with estimated fault severity levels, which drastically reduces the fault parameter space. A Bayesian inference approach is then incorporated to pinpoint the fault location and severity with high computational efficiency. The proposed approach is examined and validated through case studies.

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 underdetermined 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.

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