Structural damage identification has been continuously pursued in engineering practices to facilitate diagnosis and prognosis in structural health monitoring (SHM) systems. In SHM, the changes of modal parameters are frequently used as inputs. In this research, we employ the multiple damage location assurance criterion (MDLAC) to characterize the correlation between predictions of both frequency changes and single mode shape change with the measured data. The damage locations and severities can be obtained by maximizing the MDLAC values. Thereafter, a multi-objective optimization problem based on their MDLAC values can be formulated and optimized by applying a newly devised multi-objective DIRECT approach. The proposed approach offers practical attractions of only requiring a short amount of computational time, and the results are conclusive and repeatable.
The piezoelectric impedance-based damage identification, which traditionally uses the inverse optimization with sensitivity matrix, can in theory identify the damage location and damage severity at the same time. However, the inverse problem is underdetermined in most cases, since the number of unknowns (i.e., possible damage locations and severities) is far more than that of useful measurements. Recently, a new damage identification strategy has been proposed, which is based on Bayesian inference framework. This strategy necessitates employing forward analysis-based instead of inverse-based identification procedures. As the Bayesian inference is sampling-based algorithm, it requires repeated evaluation (FEA analysis) in the parameter space. Since in most cases, one has no prior information on the damage location and severity, the Bayesian inference should be carried out in a large parameter space that will need extremely high computing cost. In this research, a robust and efficient damage identification procedure by employing piezoelectric impedance is developed by combining the sensitivity matrix analysis and the Bayesian inference framework. The new procedure includes two steps. In the first step, a group of possible damage locations and corresponding severity are predicted based on sensitivity matrix analysis, which however does not require solving the inverse of the sensitivity matrix. The prediction from the first step is then used as the sample space. Subsequently, Bayesian inference is carried out to further determine the damage location and severity. Numerical simulations are carried out to demonstrate the damage identification performance.
Structural damage identification is a challenging subject in the structural health monitoring research. The piezoelectric impedance-based damage identification, which usually utilizes the matrix inverse-based optimization, may in theory identify the damage location and damage severity. However, the sensitivity matrix is oftentimes ill-conditioned in practice, since the number of unknowns may far exceed the useful measurements/inputs. In this research, a new method based on intelligent inference framework for damage identification is presented. Bayesian inference is used to directly predict damage location and severity using impedance measurement through forward prediction and comparison. Gaussian process is employed to enrich the forward analysis result, thereby reducing computational cost. Case study is carried out to illustrate the identification performance.
Magnetic transducers have been applied in impedance-based damage detection recently. Owing to the
magneto-mechanical coupling characteristics between a magnetic transducer and the underneath metallic structure, a
magnetic transducer can excite the host structure by means of the Lorenz force, and its electrical impedance is
directly related to the host structure’s mechanical impedance. Therefore, the change of electrical impedance before
and after damage occurrence can be used as damage indicator. Since there is no direct contact between the magnetic
transducer and the host structure, it appears that the magnetic transducer has advantage in online health monitoring
of many structures with complex geometries and boundaries. However, one key issue is that the coupling between
the magnetic transducer and the host structure is strongly influenced by the lift-off distance (i.e. the distance from
the transducer to the host structure) which changes as the structure is inevitably subject to oscillation/movement due
to environment disturbance. In this research, we propose a new approach of transformed impedance that can
explicitly take the lift-off distance change into consideration to facilitate efficient and robust decision making. This
algorithm takes advantage of the lift-off distance embedded in the impedance measurement, and is capable of
removing the lift-off variation without explicitly measuring the lift-off variation. Numerical simulations and
experimental validations are carried out to demonstrate the effectiveness.
In this research, piezoelectric transducers are incorporated in an impedance-based damage detection approach
for railway track health monitoring. The impedance-based damage detection approach utilizes the direct
relationship between the mechanical impedance of the track and electrical impedance of the piezoelectric transducer
bonded. The effect of damage is shown in the change of a healthy impedance curve to an altered, damaged curve.
Using a normalized relative difference outlier analysis, the occurrences of various damages on the track are
determined. Furthermore, the integration of inductive circuitry with the piezoelectric transducer is found to be able
to considerably increase overall damage detection sensitivity.
Magnetic transducers have been applied in a number of diverse fields including non-contact measurement, active damping and non-destructive evaluation. Recently, due to the magneto-mechanical coupling characteristics between a magnetic transducer and the underneath metallic structure, such type of transducers is employed in impedance-based damage detection schemes, which can facilitate damage detection in a non-contact manner, and have potential advantages in monitoring structures with complicated geometries and boundaries. In this research, we formulate detailed first-principle-based modeling of the magnetic impedance transducer. In particular, we focus on accurately modeling the coupling between the impedance of the magnetic transducer and the electrical and structural impedance of the host metallic structure. The modeling and analysis are validated by experimental studies.