Temperature variation effect compensation in impedance-based structural health monitoring using neural networks

Sepehry, Naserodin; Shamshirsaz, Mahnaz; Abdollahi, Farzaneh

doi:10.1177/1045389x11421814

Cited by 41 publications

(33 citation statements)

References 19 publications

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“…As already shown in Eqs. (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17), the model itself includes a bunch of parameters. Although most of them cannot be changed due to damage (e.g., nominal radius of the PWAS), these parameters will have some variations resulting from different batches, different bonding procedures, and so on.…”

Section: Model-based Methodsmentioning

confidence: 99%

“…The effort for this method is large, as the temperature dependence of all significant parameters has to be included within the model. Other efforts to compensate for varying environmental and operational conditions for signal-based techniques within wave-and vibration-based SHM systems use statistical damage classification [6,7] including, e.g., fuzzy classification [8], the use of neural networks [9] and self-organizing maps [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Failure Assessment of Piezoelectric Actuators and Sensors for Increased Reliability of SHM Systems

Mueller¹,

Fritzen²

2018

Structural Health Monitoring From Sensing to Processing

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In the chain from sensing to information extraction, there are many traps where errors can occur, which might lead to false alarms and therefore leave us with the impression of an unreliable system. In this chapter, we deal with the important first element of the chain, the sensor, which can undergo various faults and defects during its lifetime. Especially for the use of acousto-ultrasonic (AU)-based methods or the electro-mechanical impedance (EMI) method, piezoelectric transducers are frequently used. Subsequent steps within the chain of SHM rely on the quality and reliability of these measurements. An overview is given on the usage of piezoelectric transducers within SHM systems, their electromechanical coupling and its modeling as well as frequent faults of these devices and methods on how to inspect them and diagnose defects. The authors show the effects of different transducer faults on the excited wave field, used for AU. It is shown how a sensor fault can be detected before the SHM system indicates a (false) alarm. With the help of application scenarios-including temperature variations-the advantages and disadvantages of the introduced methods of transducer inspection are presented, enabling an increased reliability of SHM systems.

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Section: Model-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Failure Assessment of Piezoelectric Actuators and Sensors for Increased Reliability of SHM Systems

Mueller¹,

Fritzen²

2018

Structural Health Monitoring From Sensing to Processing

View full text Add to dashboard Cite

show abstract

“…Lim et al used kernel principle component analysis (KPCA) to improve damage detectability under varying temperature conditions and minimized false alarms. Additionally, ANN‐based methods were also effective in compensating for the temperature variation in EMI‐based damage detection methods …”

Section: Introductionmentioning

confidence: 99%

“…Additionally, ANN-based methods were also effective in compensating for the temperature variation in EMI-based damage detection methods. 45 Icing is a large-scale weather phenomenon and covers almost the entire surface of all stay cables in a bridge. On a smaller scale, ice covering the piezoelectric sensor significantly influences the local mass, stiffness, and damping.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Zhou

2019

Struct Control Health Monit

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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.

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“…High sensitivity damage detection using piezoelectric material has been widely used in structural health monitoring (SHM) for decades. Two main methods based on using piezoelectric wafer active sensor (PWAS) in SHM are Lamb wave and electromechanical impedance . Electromechanical impedance method is applied in frequency domain for damage detection, and Lamb wave method is used in time domain for detection and also localization of damage.…”

Section: Introductionmentioning

confidence: 99%

Low-cost simulation using model order reduction in structural health monitoring: Application of balanced proper orthogonal decomposition

Sepehry

Shamshirsaz

Bakhtiari-Nejad

2017

Struct Control Health Monit

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Summary In this paper, both two powerful methods in structural health monitoring, Lamb wave propagation and electromechanical impedance method, are modeled, implemented, and tested to inspect the plate‐like structure using piezoelectric wafer active sensor (PWAS). In order to detect damage in structure, introducing a model for advanced signal processing algorithm is essential. A three‐dimensional spectral finite element method has been applied to model Lamb wave propagation and electromechanical impedance in plate with attached PWAS. In reality, Lamb wave generation and electromechanical impedance in high frequencies lead to a high degree of freedom in modeling and consequently to a low speed simulation in frequency and time domains calculation. For us to overcome this problem, balanced proper orthogonal decomposition (BPOD) has been developed and used as model order reduction for these methods in structural health monitoring. The experimental tests are carried out on aluminum plate with two attached PWAS. The simulation results obtained by BPOD and full‐order method are validated by comparison with experimental ones. The results show that the proposed and implemented model order reduction method (BPOD) leads to increase significantly simulation speed without any distortion in accuracy. For Lamb wave method, CPU time consuming using BPOD is reduced 5.8 times (frequencies 40 and 150 kHz) comparing to full‐order model application without any alteration of accuracy (less than 0.03 normalized voltage). For impedance method, the simulation time has been decreased 10 times less than using full‐order model in frequency range 90–100 kHz while the error of impedance real part remains less than 0.025.

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Temperature variation effect compensation in impedance-based structural health monitoring using neural networks

Cited by 41 publications

References 19 publications

Failure Assessment of Piezoelectric Actuators and Sensors for Increased Reliability of SHM Systems

Failure Assessment of Piezoelectric Actuators and Sensors for Increased Reliability of SHM Systems

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

Low-cost simulation using model order reduction in structural health monitoring: Application of balanced proper orthogonal decomposition

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