This paper presents an overview and recent advances in impedance-based structural health monitoring. The basic principle behind this technique is to apply high-frequency structural excitations (typically greater than 30 kHz) through surface-bonded piezoelectric transducers, and measure the impedance of structures by monitoring the current and voltage applied to the piezoelectric transducers. Changes in impedance indicate changes in the structure, which in turn can indicate that damage has occurred. An experimental study is presented to demonstrate how this technique can be used to detect structural damage in real time. Signal processing methods that address damage classifications and data compression issues associated with the use of the impedance methods are also summarized. Finally, a modified frequency-domain autoregressive model with exogenous inputs (ARX) is described. The frequency-domain ARX model, constructed by measured impedance data, is used to diagnose structural damage with levels of statistical confidence.

Structural health monitoring using piezoelectric impedance measurements.

Structural Health Monitoring (SHM) is a valuable tool for in-service assessment of structural condition. Despite a broad use in many engineering fields, SHM has seen limited application in space systems. This article explores specifics of SHM applied to space systems and satellites in particular. It is suggested that SHM may be considered for aiding rapid assembly of spacecraft components, monitoring system dynamics during launch, and model updating from an assessment of in-service variation of structural properties. The article presents a discussion of factors affecting realization of the SHM system for spacecraft and provides recommendations for the system configuration and its practical use. The SHM system design based on a network of piezoelectric active sensors is considered. System operation focuses on SHM of improperly tightened bolts, assessment of adhesive bonds, and embedded material characterization techniques. Synergistic use of the same hardware for acoustoelastic, non-linear acoustic, and ma...

Piezoelectric Wafer Active Sensor Structural Health Monitoring of Space Structures

An overview of recent advances in electromechanical impedance- (EMI-) based structural health monitoring is presented in this paper. The basic principle of the EMI method is to use high-frequency excitation to sense the local area of a structure. Changes in impedance indicate changes in the structure, which in turn indicate that damages appear. An accurate EMI model based on the method of reverberation-ray matrix is introduced to correlate changes in the signatures to physical parameters of structures for damage detection. Comparison with other numerical results and experimental data validates the present model. A brief remark of the feasibility of implementing the EMI method is considered and the effects of some physical parameters on EMI technique are also discussed.

https://downloads.hindawi.com/journals/ace/2010/429148.pdf

Structural Health Monitoring Using High-Frequency Electromechanical Impedance Signatures

Given the present burdens associated with inspection and maintenance of Civil Infrastructure, the development of effective, automated damage diagnosis techniques, including the sensor technologies that support them, has become a major research need. While recent developments in wireless sensor networks have demonstrated their potential to provide continuous structural response data to quantitatively assess structural health, many important issues including network lifetime and stability, damage detection reliability, and trade-offs in model order to balance computational capabilities must be realistically addressed. Only then can wireless embedded sensor networks become a practical tool for Structural Health Monitoring of large, complex Civil Structures. In response to these needs, the concept of a multi-scale wireless sensor network is introduced in this study with a restricted input network activation scheme and the integration of data from a heterogeneous sensor array to improve damage detection for low-order models. The multi-scale network concept introduced here helps to improve power efficiency, minimize packet loss and latency, and eliminate synchronization issues through the use of a decentralized analysis scheme and the activation of sub-networks only in the vicinity of suspected damage, while reducing the required size of the reference pool for the undamaged state. This study introduces the network architecture concept, a strain-driven approach to damage detection and preliminary simulated results.

/pdf/wireless-sensor-networks-for-structural-health-monitoring-a-7fk622hd2u.pdf

Wireless Sensor Networks for Structural Health Monitoring: A Multi-Scale Approach

For the safety of prestressed structures such as cable-stayed bridges and prestressed concrete bridges, it is very important to ensure the prestress force of cable or tendon. The loss of prestress force could significantly reduce load carrying capacity of the structure and even result in structural collapse. The objective of this study is to present a smart PZT-interface for wireless impedance-based prestress-loss monitoring in tendon-anchorage connection. Firstly, a smart PZT-interface is newly designed for sensitively monitoring of electro-mechanical impedance changes in tendon-anchorage subsystem. To analyze the effect of prestress force, an analytical model of tendon-anchorage is described regarding to the relationship between prestress force and structural parameters of the anchorage contact region. Based on the analytical model, an impedance-based method for monitoring of prestress-loss is conducted using the impedance-sensitive PZT-interface. Secondly, wireless impedance sensor node working on Imote2 platforms, which is interacted with the smart PZT-interface, is outlined. Finally, experiment on a lab-scale tendon-anchorage of a prestressed concrete girder is conducted to evaluate the performance of the smart PZT-interface along with the wireless impedance sensor node on prestress-loss detection. Frequency shift and cross correlation deviation of impedance signature are utilized to estimate impedance variation due to prestress-loss.

Smart PZT-interface for wireless impedance-based prestress-loss monitoring in tendon-anchorage connection

In this paper, the applicability of an auto-regressive model with exogenous inputs (ARX) in the frequency domain to structural health monitoring (SHM) is established. Damage sensitive features that explicitly consider non-linear system input/output relationships are extracted from the ARX model. Furthermore, because of the non-Gaussian nature of the extracted features, Extreme Value Statistics (EVS) is employed to develop a robust damage classifier. EVS provides superior performance to standard statistical methods because the data of interest are in the tails (extremes) of the damage sensitive feature distribution. The suitability of the ARX model, combined with EVS, to non-linear damage detection is demonstrated using vibration data obtained from a laboratory experiment of a three-story building model. It is found that the vibration-based method, while able to discern when damage is present in the structure, is unable to localize the damage to a particular joint. An impedance-based active sensing method using piezoelectric (PZT) material as both an actuator and a sensor is then investigated as an alternative solution to the problem of damage localization. Copyright © 2005 John Wiley & Sons, Ltd.

Active sensing using impedance-based ARX models and extreme value statistics for damage detection

Recent research has shown that chaotic structural excitation and state space reconstruction may be used beneficially in structural health monitoring (SHM). The focus of this study is to apply a chaotic waveform to a piezoelectric (PZT) patch that is bonded to a test structure. The use of high frequency chaotic excitation (~80 kHz) combined with PZT active sensing allows the location of incipient damage in the structure to be easily identified. The generation of high frequency chaos from a low frequency process necessitates bandwidth up-conversion that has been shown to be dimension preserving. We investigate the use of this method in conjunction with a novel prediction error algorithm to determine the damage state of a frame structure.

Piezoelectric active sensing using chaotic excitations and state space reconstruction

Chaotic insonification for health monitoring of an adhesively bonded composite stiffened panel

An adhesive bond state classification method for a composite skin-to-spar joint using chaotic insonification

Recent research has shown that chaotic structural excitation and state space reconstruction may be used beneficially
in structural health monitoring (SHM) processes. This relationship has been exploited for use in detection of bolt
preload reduction by using a chaotic waveform with ultrasonic frequency content with a damage detection algorithm
based on auto-regressive (AR) modeling. The signal is actively applied to a structure using a bonded macro fiber
composite (MFC) patch. The response generated by the mechanical interaction of the MFC patch with the structure is
then measured by other affixed MFC patches. In this study the suitability of particular chaotic waveforms will be
investigated through the use of evolutionary algorithms. These algorithms are able to find an optimum excitation for
maximum damage state discernability whose fitness is two orders of magnitude greater than choosing random
parameters for signal creation.

Timothy R. Fasel

Papers

Active sensing using impedance-based ARX models and extreme value statistics for damage detection

Piezoelectric active sensing using chaotic excitations and state space reconstruction

Chaotic insonification for health monitoring of an adhesively bonded composite stiffened panel

An adhesive bond state classification method for a composite skin-to-spar joint using chaotic insonification

Optimized Guided Wave Excitations for Health Monitoring of a Bolted Joint