Electromechanical impedance‐based ice detection of stay cables with temperature compensation

Abstract

Summary Ice accretion on stay cables is a critical problem for cable‐stayed bridges in a cold climate. Timely and reliable ice detection is a primary and necessary measure for all types of de‐icing solutions. In the study, electromechanical impedance (EMI) method is first proposed to detect ice accretion on the stay cables. This method is extremely sensitive to the small changes in local mass, local stiffness, and temperature, and all of the aforementioned are involved in the icing process on stay cables. With respect to ice detection by using the EMI method, the changes in structural stiffness and mass due to the ice covered are considered as primary characteristics, whereas the change in temperature corresponds to the disturbing factor. Thus, temperature compensation should be implemented during the post processing of experimental data. The proposed method is demonstrated by experiments on a segment of a full‐scale stay cable and with necessary signal processing. The icing process is simulated by spraying water in a low temperature laboratory. The root mean square deviation, relative root mean square deviation, and the characteristic bandwidth are used to quantitatively evaluate the ice accretion. Subsequently, more accurate ice thickness identification is implemented through a trained artificial neural network model. Results indicate that the proposed EMI method with temperature compensation is rapid and feasible for the ice detection of stay cables.

Structural Damage Recognition Based on the Finite Element Method and Quantum Particle Swarm Optimization Algorithm

“…Based on EMI detection method, the traditional manner is mainly to adopt kinds of damage indexes based on a statistical analysis of impedance/admittance -frequency spectra, which can describe the damage existence and development. Then various intelligent algorithms like an artificial neural network [11,29], support vector machine [10], and genetic algorithm [30] are all introduced to improve the damage identification accuracy, which all can approximately quantitative characterize damage status but cannot calculate exactly the damage parameters. To solve some structural damage parameters is a typical inverse problem, and it is usually a nonlinear problem, which is difficult to be solved by solving equations.…”

“…Park et al [10] realized the discrimination of two types of damages by combining the Lamb method with support vector machine, but compared to the EMI (electromechanical impedance, EMI) method, the Lamb technology needs to combine relatively complex circuit system because of its high excitation voltage, severe energy dissipation, and multiple signal reflections, etc. Therefore, the author of Reference [11] combined the EMI technology and ANN (artificial neural network, ANN) to recognize changes in the structural surface, in which a large number of training samples are required. For damage parameters identification, there are many feature selection methods like firefly algorithm [12], PSO (particle swarm optimization, PSO) [13], differential evolution [14], and genetic algorithm [15], but in this paper, the EMI method sensitive to a structural state change, combined with the AI optimization algorithm based on the concept of PSO is first proposed to recognize minor structural damages, which is mainly because that the PSO algorithm has the characteristics of fewer parameters, simple implementation, fast computing speed, etc.…”

Piezoelectric‐based hoop‐type interface for impedance monitoring of local strand breakage in prestressed multi‐strand anchorage

“…Because material properties of both an inspected structure and PZT sensors are temperature-dependent (i.e., the thermal expansion coefficient and the dielectric coefficient 37 ), any environmental changes could cause variations in impedance features. [38][39][40] Since a damage F I G U R E 1 7 Root-mean-squaredeviation (RMSD) indices calculated for Peak 1's impedance of the near-bottom lead zirconate titanate (PZT) sensors indicator (e.g., RMSD) of two impedance signals is computed based on signals measured at two different times, the damage index could be larger than the control threshold (i.e., UCL) despite no occurrence of damage. It could lead to fault strand-breakage detection results unless temperature-induced impedance variations compensated adequately, which demands further research.…”

“…In fact, real structures exist in an environment of ambient temperature‐induced uncertainties. Because material properties of both an inspected structure and PZT sensors are temperature‐dependent (i.e., the thermal expansion coefficient and the dielectric coefficient 37 ), any environmental changes could cause variations in impedance features 38–40 . Since a damage indicator (e.g., RMSD) of two impedance signals is computed based on signals measured at two different times, the damage index could be larger than the control threshold (i.e., UCL) despite no occurrence of damage.…”

Electromechanical Impedance Based Self-Diagnosis of Piezoelectric Smart Structure Using Principal Component Analysis and LibSVM

“…To date, a lot of researches on piezoelectric smart structure have been carried out based on the good piezoelectric lead zirconate titanate (PZT) patch properties [4][5][6][7][8] . With the popularization and service life extension of piezoelectric smart structure in practical structural health monitoring (SHM), the influence of the sensor state on measurement results has become a problem that cannot be neglected.…”