Fault parameters in a structure are identified by matching measurements with model predictions in the parametric space. As high frequency measurements are preferred to uncover small-sized damage, piezoelectric impedance/admittance active interrogation has shown promising aspects. Nevertheless, challenges remain. The amount of useful measurement information is generally insufficient to pinpoint damage. The inverse identification is usually underdetermined. In this research, we develop a combinatorial enhancement to tackle these challenges. A tunable piezoelectric impedance sensing procedure is developed in which an adaptive inductor element is integrated with the piezoelectric transducer, which will lead to significantly enriched measurement data for the same damage. Subsequently, an intelligent learning automata-based multi-objective particle swarm optimization framework is synthesized to inversely identify the damage location and severity. Case studies are conducted to highlight the accuracy of the damage identification.
Structural damage identification using the impedance/admittance measurements of a piezoelectric transducer can be converted into a multi-objective optimization framework targeting the minimization of the discrepancy between prediction and experimental measurements, with damage locations and severities as unknown variables. However, the unknowns are usually on a large scale and show sparse characteristics since the damage only occurs at a small area. This places the burden on the optimization algorithms in the identification process. Here, a sparse initialization algorithm is introduced to generate a sparse population for the large-scale variables to tackle the challenge. The algorithm is combined with the particle swarm algorithm to locate and quantify the damage for verification purposes. Several cases are considered, and the results show that the algorithm can generate high-quality damage identification solutions with limited simulation and experimental measurements.
KEYWORDS: Optimization (mathematics), Transducers, Algorithm development, Finite element methods, Structural health monitoring, Detection and tracking algorithms, Process modeling, Stochastic processes, Image segmentation, Analytical research
In this research, we report a new fault identification algorithm utilizing multi-objective optimization. Fault identification problem is commonly under-determined, as measurement information may not be sufficient to facilitate a direct inversion. We formulate an optimization problem, aiming at minimizing the discrepancy between model prediction and measurement. This yields multiple possible fault scenarios, which lays down foundation for further inspection.
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