Abstract
Using piezoelectric impedance/admittance sensing for structural health monitoring is promising, owing to the simplicity in circuitry design as well as the high-frequency interrogation capability. The actual identification of fault location and severity using impedance/admittance measurements, nevertheless, remains to be an extremely challenging task. A first-principle-based structural model using finite element discretization requires high dimensionality to characterize the high-frequency response. As such, direct inversion using the sensitivity matrix usually yields an under-determined problem. Alternatively, the identification problem may be cast into an optimization framework, in which the fault parameters are identified through the repeated forward finite element analysis that is often computationally prohibitive. This paper presents an efficient data-assisted optimization approach for fault identification without using the finite element model iteratively. We formulate a many-objective optimization problem to identify the fault parameters, where response surfaces of impedance measurements are constructed through the Gaussian process-based calibration. To balance between the solution diversity and convergence, an e-dominance-enabled many-objective simulated annealing algorithm is established. As multiple solutions are expected, a voting score calculation procedure is developed to further identify those solutions that yield better implications regarding a structural health condition. The effectiveness of the proposed approach is demonstrated by the systematic numerical and experimental case studies.
Highlights
The timely and accurate identification of faults in aerospace, mechanical, marine, and infrastructure systems has received significant recent attention
To address the fundamental challenges posed by the under-determined problem formulation and model-based sensitivity approximation, we cast the damage identification problem into a many-objective optimization by reconstructing impedance response surfaces as objective functions utilizing training data
The optimization problem is tackled by an ε-dominance enabled many-objective simulated annealing algorithm
Summary
The timely and accurate identification of faults in aerospace, mechanical, marine, and infrastructure systems has received significant recent attention. The key issues are: 1) how to effectively generate high-frequency sensing data; and 2) how to efficiently and accurately identify fault location and severity from the data [35] Owing to their two-way electro-mechanical coupling, piezoelectric transducers are commonly used in structural health monitoring [15], [33]. The novelty of this new framework is multifold This is the first research effort to use response surfaces of Gaussian process as objective functions for optimization, whereas the solution (i.e., fault identification result) is obtained by combining multi-objective Simulated Annealing with ε-dominance. In impedance/admittance-based fault identification, as harmonic voltage excitation is supplied for active sensing, Equation (5) is used iteratively giving different read of Y (ω, α) when the excitation frequency is swept within certain ranges that cover a number of structural resonances around which physical measurements are taken. It is worth noting that the active-sensing fault identification framework is effective in giving implications of severity and location of the fault by means of stiffness change but limited in distinguishing the exact type
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