Structural damage identification using piezoelectric impedance measurement with sparse inverse analysis

Cao, Pei; Qi, Shengwen; Tang, Jiong

doi:10.1088/1361-665x/aaacba

Cited by 32 publications

(29 citation statements)

References 34 publications

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“…A series of papers [5,6] discusses applications of an extension of direct to damage identification problems with a numerical comparison with MO-DIRECT [1,45]. The algorithm proposed uses a scalarization of the multiobjective optimization problem by assigning to a vector v in the objective space the rank R(v) given by the nondominated sorting.…”

Section: Literature Surveymentioning

confidence: 99%

On the Extension of the DIRECT Algorithm to Multiple Objectives

Lovison

Miettinen

2020

J Glob Optim

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Deterministic global optimization algorithms like Piyavskii-Shubert, direct, ego and many more, have a recognized standing, for problems with many local optima. Although many single objective optimization algorithms have been extended to multiple objectives, completely deterministic algorithms for nonlinear problems with guarantees of convergence to global Pareto optimality are still missing. For instance, deterministic algorithms usually make use of some form of scalarization, which may lead to incomplete representations of the Pareto optimal set. Thus, all global Pareto optima may not be obtained, especially in nonconvex cases. On the other hand, algorithms attempting to produce representations of the globally Pareto optimal set are usually based on heuristics. We analyze the concept of global convergence for multiobjective optimization algorithms and propose a convergence criterion based on the Hausdorff distance in the decision space. Under this light, we consider the well-known global optimization algorithm direct, analyze the available algorithms in the literature that extend direct to multiple objectives and discuss possible alternatives. In particular, we propose a novel definition for the notion of potential Pareto optimality extending the notion of potential optimality defined in direct. We also discuss its advantages and disadvantages when compared with algorithms existing in the literature.

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Section: Literature Surveymentioning

confidence: 99%

On the Extension of the DIRECT Algorithm to Multiple Objectives

Lovison

Miettinen

2020

J Glob Optim

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“…Recently, sparse regularization (or the l 1 norm regularization technique) based damage identification method has been developed to overcome the abovementioned limitation [ 41 , 42 ]. Cao, et al [ 43 ] developed the EMI based structural damage detection method using model updating incorporated with the l 0 norm regularization technique. The multi-objective Dividing RECTangles (DIRECT) algorithm was implemented to solve the multi-objective optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Damage Identification in Plate Structures Using Sparse Regularization Based Electromechanical Impedance Technique

Fan

2020

Sensors

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This paper proposes a novel structural damage quantification approach using a sparse regularization based electromechanical impedance (EMI) technique. Minor structural damage in plate structures by using the measurement of only a single surface bonded lead zirconate titanate piezoelectric (PZT) transducer was quantified. To overcome the limitations of using model-based EMI based methods in damage detection of complex or relatively large-scale structures, a three-dimensional finite element model for simulating the PZT–structure interaction is developed and calibrated with experimental results. Based on the sensitivities of the resonance frequency shifts of the impedance responses with respect to the physical parameters of plate structures, sparse regularization was applied to conduct the undetermined inverse identification of structural damage. The difference between the measured and analytically obtained impedance responses was calculated and used for identification. In this study, only a limited number of the resonance frequency shifts were obtained from the selected frequency range for damage identification of plate structures with numerous elements. The results demonstrate a better performance than those from the conventional Tikhonov regularization based methods in conducting inverse identification for damage quantification. Experimental studies on an aluminum plate were conducted to investigate the effectiveness and accuracy of the proposed approach. To test the robustness of the proposed approach, the identification results of a plate structure under varying temperature conditions are also presented.

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“…One potential way to avoid the direct inversion is to convert the identification problem into an optimization problem, where possible property changes in all segments are treated as design parameters. These parameters are updated by minimizing the discrepancy between sensor measurements and model predictions through various optimization techniques in which only forward analyses are performed [2], [6], [7], [27]. The necessary computational cost, however, could be very high.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Gaussian Process Regression and Many-Objective Optimization Through Voting Scores for Fault Identification

Cao

Tang

2019

IEEE Access

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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 ε-dominance-enabled manyobjective 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.

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Structural damage identification using piezoelectric impedance measurement with sparse inverse analysis

Cited by 32 publications

References 34 publications

On the Extension of the DIRECT Algorithm to Multiple Objectives

On the Extension of the DIRECT Algorithm to Multiple Objectives

Damage Identification in Plate Structures Using Sparse Regularization Based Electromechanical Impedance Technique

Leveraging Gaussian Process Regression and Many-Objective Optimization Through Voting Scores for Fault Identification

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