Impedance-based Structural Health Monitoring Technique Research Articles

Piezo impedance-based monitoring of loosening of bolts: Experimental and numerical study

The suspension strut mount plays a crucial role in any vehicular suspension system, where it acts as a connector (bolted) to the vehicular body and suspension strut. The mount’s purpose is to cushion the Vehicular impacts and reduce the jarring effect, noise, and vibration caused due to vehicle movement over the undulated roads. The self-loosening of bolts results because of the up and down impact of the spring cause the jounce bouncer to push and pull action at the mount interface, cause vibrations transmitted to the vehicle camber. Self-loosening leads to damage of mount followed by clunking noises, noisy steering, tire misalignment, and can cause discomfort to the passenger. Therefore, condition monitoring and assessment of an upper strut mount is necessary for vehicles. This paper studies the feasibility of the piezo Impedance-based Structural health monitoring (SHM) technique to monitor the self-loosening bolts in the upper strut mount of the suspension system (MacPherson strut suspension) of passenger car. The piezo coupled signatures were obtained experimentally by loosening all the three bolts (connected to strut bearing) through control torques through a digital torque wrench. All the experimental signatures were acquired with a single PZT patch bonded to the surface of the upper strut mount for loosening bolts with pre-tight loss. Progressive damage scenarios are simulated along with preload loss of either single bolt or all three bolts, respectively. Three different statistical damage indices were evaluated for damage quantification raised due to bolt loosening. A 3D numerical modeling of strut mount is done using ANSYS WORKBENCH, and piezo impedance signatures were acquired (hence converted to admittance) for validating the experimental signatures. In an overall, this study provides an insight into the loss of structural integrity due to the self-loosening of suspension bolts, which can be threatful to vehicular integrity.

Convolutional neural network and impedance-based SHM applied to damage detection

The impedance-based structural health monitoring technique uses measured signatures changes to identify incipient damages in structures. The purpose is to perform a correlation of these changes with the physical phenomena. However, since electromechanical coupling exists, some environmental influences such as temperature changes may lead to false decision regarding the condition of the structure. As a result, innovative machine learning tools have been extensively investigated to avoid errors in structural prognosis and, in this sense, recent applications of convolutional neural networks (CNN) have emerged within the scope of SHM research, focusing mainly on vibration analysis. However, studies that aim to combine neural architectures with intelligent materials for structural monitoring purposes have been poorly evaluated. Consequently, its integration with the electromechanical impedance method is still considered as being a new application of CNN. Thus, in order to contribute to the SHM area, this work presents a combination of the CNN architecture and the EMI methodology. In the present contribution, three aluminum beams subjected to three different steady temperature levels (0 °C, 10 °C and 20 °C) were studied. For this aim, a test chamber was used for humidity and temperature control. Artificial damages such as mass addition were taken into account so that impedance signatures related to both pristine and damaged conditions can be analyzed. Thus, a one-dimensional Convolutional Neural Network (1D CNN) was designed, trained and used for damage prediction purposes. In this context, a temperature robust model that is able to identify damage independently of environmental condition was developed.

Debonding Damage Detection in CFRP Plate-Strengthened Steel Beam Using Electromechanical Impedance Technique.

Carbon fiber reinforced polymer materials are widely applied in structure strengthened engineering because of the many advantages of carbon fiber reinforced polymer. However, the debonding damage between the carbon fiber reinforced polymer and host structures occurs frequently, which might lead to the brittle failure of structure components, especially flexural ones. In this paper, an electromechanical impedance-based method, an important technique in structural health monitoring, was adopted to detect the debonding damage of carbon fiber reinforced polymer plate-strengthened steel beam by using lead zirconate titanate (PZT) transducers. A carbon fiber reinforced polymer plate-strengthened steel beam specimen was fabricated in the laboratory and two PZT sensors were attached at different locations on the carbon fiber reinforced polymer plate. The impedance signatures with variation of the different degrees of the debonding damage were measured by an impedance analyzer. The root-mean-square deviation method and the cross-correlation coefficient method were used to quantify the correlation between the electromechanical impedance and the debonding damage degree. The results reflect that an electromechanical impedance-based structural health monitoring technique can serve as a good method to detect the debonding damage of carbon fiber reinforced polymer plate-strengthened steel structures.

Impedance-based structural health monitoring and statistical method for threshold-level determination applied to 2024-T3 aluminum panels under varying temperature

The impedance-based structural health monitoring method has become a promising and attractive tool for damage identification and is considered a nondestructive evaluation technique. However, conventional impedance-based structural health monitoring studies have mainly focused on structural damage identification but not so much on statistical modeling approaches in order to determine a threshold for the decision making of the damage detection system. In this study, the impedance-based structural health monitoring technique is used in a damage detection problem considering temperature variation effects. For this aim, three aluminum 2024-T3 plates were instrumented with small lead zirconate titanate patches close to their borders, and damage was introduced in the central position of the plates, with temperature ranging from −10°C to 60°C. This article proposes a method to statistically determine a threshold for damage detection purposes using concepts of statistical process control, as well as confidence intervals and normality tests in order to obtain a diagnosis with a previously determined confidence level. Thus, this work presents a sensitivity evaluation of the impedance-based structural health monitoring technique as applied to aluminum plates under varying temperature. With the technique proposed, damage threshold levels are determined so that lead zirconate titanate patches placed approximately 280 mm from the damage inserted were able to detect saw cuts of approximately 7 mm long, with 95% confidence intervals inside the temperature range considered.

A cost-effective impedance-based structural health monitoring technique for steel structures by monitoring multiple areas

Throughout the ages, ensuring structural safety has always been a vital task as civil infrastructures deteriorate due to the surrounding environment with time. Although various non-destructive techniques exist nowadays for maintaining the structural integrity, most of the techniques require expensive equipment with highly trained experts. In addition, onsite techniques may require a downtime period of the structure, causing inconvenience for the public. The purpose of this study is to introduce a cost-effective, impedance-based health monitoring approach using the electromechanical impedance technique. Since one device with a piezoelectric material is used to monitor a single area, this can be extremely costly when covering large areas such as bridges and buildings. To overcome this problem, a technique is introduced in this study for one device to monitor multiple areas, significantly reducing the equipment cost when covering large areas.

Probabilistic Neural Network and Fuzzy Cluster Analysis Methods Applied to Impedance-Based SHM for Damage Classification

Impedance-based structural health monitoring technique is performed by measuring the variation of the electromechanical impedance of the structure caused by the presence of damage. The impedance signals are collected from patches of piezoelectric material bonded on the surface of the structure (or embedded). Through these piezoceramic sensor-actuators, the electromechanical impedance, which is directly related to the mechanical impedance of the structure, is obtained. Based on the variation of the impedance signals, the presence of damage can be detected. A particular damage metric is used to quantify the damage. Distinguishing damage groups from a universe containing different types of damage is a major challenge in structural health monitoring. There are several types of failures that can occur in a given structure, such as cracks, fissures, loss of mechanical components (e.g., rivets), corrosion, and wear. It is important to characterize each type of damage from the impedance signals considered. In the present paper, probabilistic neural network and fuzzy cluster analysis methods are used for identification, localization, and classification of two types of damage, namely, cracks and rivet losses. The results show that probabilistic neural network and fuzzy cluster analysis methods are useful for identification, localization, and classification of these types of damage.

Impedance-based structural health monitoring incorporating neural network technique for identification of damage type and severity

Impedance-based structural health monitoring (SHM) has come to the forefront in the SHM community because of its practical potential for real applications. In the impedance-based SHM technique, it is very important to select the optimal frequency range most sensitive to the expected structural damage, and more quantitative information on the structural damages might be needed compared to the conventional damage index. Therefore, this study proposes an innovative neural network (NN)-based pattern analysis tool (1) to identify damage-sensitive frequency ranges autonomously and (2) to provide detailed information such as the damage type and severity. The importance of selecting the optimal frequency range was first investigated experimentally using a simply-supported aluminum beam. The performance of the proposed NN-based approach was validated throughout damage identifications of loose bolts and notches on a bolt-jointed aluminum beam and a lab-scale pipe structure. Finally, the proposed NN-based algorithm was embedded into a wireless impedance sensor node to detect real damage in a full-scale bridge. Overall, the proposed approach incorporating a wireless impedance sensor node was used to evaluate the damage type and severity in multi-type and multiple structural damage cases.

0.18 [micro sign]m CMOS integrated circuit design for impedance-based structural health monitoring

Piezoelectric materials constitute a class of intelligent materials that are helpful for monitoring the structural integrity. The principle of the piezoelectric impedance-based structural health monitoring technique is to measure the electrical impedance of a piezoelectric patch attached to a structure in a certain frequency range. Electrical impedance variations indicate physical changes in the structure due to the coupling between the electrical impedance and the mechanical impedance. Traditional methods use an impedance analyser that increases the inspection cost. The objective of this work is to introduce an electronic circuit for the piezoelectric impedance-based structural health monitoring. The circuit can monitor the electrical impedance variations of a piezoelectric patch attached to a structure. The frequency range is from 7.47 to 277.29 kHz. This frequency range covers the sensitive range of the piezoelectric structural integrity. The power consumption of the circuit is 18.15 mW. The chip area is 1.03 mm x 2.30 mm. The cost of the final design will be much lower than that of an impedance analyser. Using a wireless communication circuit, a sensor network might be established in the future.

Temperature corrected sensor diagnostics for impedance-based SHM

Previously, a sensor diagnostics method has been developed for the impedance-based structural health monitoring technique. Impedance techniques utilize piezoelectric patches bonded to the structure of interest for inference of damage. Measuring the slope of piezoelectric susceptance allows unhealthy sensor to be identified. While this sensor diagnostics technique is very useful in detecting damaged sensors bonded to a structure, the method is also susceptible to temperature variations. The object of this study is to accurately provide sensor diagnostics at any temperature. The model developed should be accurate and easy to implement on health monitoring hardware. A frame structure is fabricated to simulate a real structure with complex boundary conditions for experimental testing in various thermal environments. A model predicting piezoelectric susceptance slope at any temperature is generated and validated on the frame structure in an extended temperature range.

Automated Impedance-based Structural Health Monitoring Incorporating Effective Frequency Shift for Compensating Temperature Effects

This study presents an impedance-based structural health monitoring (SHM) technique considering temperature effects. The temperature variation results in significant impedance variations, particularly a frequency shift in the impedance, which may lead to erroneous diagnostic results of real structures, such as civil, mechanical, and aerospace structures. In order to minimize the effect of the temperature variation on the impedance measurements, a previously proposed temperature compensation technique based on the cross-correlation between the reference-impedance data and a concurrent impedance data is revisited. In this study, cross-correlation coefficient (CC) after an effective frequency shift (EFS), which is defined as the frequency shift causing two impedance data to have the maximum correlation, is utilized. To promote a practical use of the proposed SHM strategy, an automated continuous monitoring framework using MATLAB® is developed and incorporated with the current hardware system. Validation of the proposed technique is carried out on a lab-sized steel truss bridge member under a temperature varying environment. It has been found that the CC values have shown significant fluctuations due to the temperature variation, even after applying the EFS method. Therefore, an outlier analysis providing the optimal decision limits under the inevitable variations has been carried out for more systematic damage detection. It has been found that the threshold level shall be properly selected considering the daily temperature range and the minimum target damage level for detection. It has been demonstrated that the proposed strategy combining the EFS and the outlier analysis can be effectively used in the automated continuous SHM of critical structural members under temperature variations.

A Circuit Design for Impedance-based Structural Health Monitoring

Systems and structures monitoring their own structural integrity have become an active field. One method uses the piezoelectric impedance-based structural health monitoring technique (SHM), which utilizes a piezoelectric patch attached to a structure and measures its impedance within a certain frequency range. The frequency is maintained in the kiloHertz range for optimum sensitivity and coverage in damage detection. Owing to the coupling between electrical impedance and mechanical impedance, electrical impedance variations can indicate physical changes in the host structure. Based on the idea of a bridge circuit, a new electronic circuit to realize electrical impedance monitoring is presented (US patent pending). The proposed circuit generates a frequency sweep from 53 to 164kHz and measures the relative value of the electrical impedance modulus of a piezoelectric patch. The circuit can be integrated into a chip combined with a piezoelectric patch attached to a structure. With the help of a communication circuit, a remote SHM system can be set up in the future.

A Smart Health Monitoring System with Application to Welded Structures using Piezoceramic and Fiber Optic Transducers

Large welded structures, including ships and offshore structures, are normally in operation under cyclic fatigue loadings. These structures include many geometric as well as material discontinuities due to weld joints, and the fatigue strength at these hot spots is of critical importance for the structural performance. In the past, various Non-Destructive Evaluation (NDE) techniques have been developed to detect fatigue cracks and to estimate their location and size. However, important limitations of most of the existing NDE methods are that they are off-line; the normal operation of the structure has to be interrupted and the device often has to be disassembled. In this context, a new impedance-based structural health monitoring system employing piezoceramic transducers is developed with a special interest in applying the technique for welded structural members in ship and offshore structures. In particular, the impedance-based structural health monitoring technique that employs the coupling effect of piezoceramic (PZT) materials and structures is investigated. Moreover, a preliminary study of using fiber Bragg grating (FBG) type optical fiber sensors for the stress measurement of a typical weld structure is investigated. The final goal is to extend the fiber optic sensor technique for the global and local stress measurements of ship or offshore structures.

Simultaneous Health Monitoring and Vibration Control of Structures Using Smart Materials

Large welded structures, including ships and offshore structures, are normally in operation under cyclic fatigue loadings. These structures include many geometric as well as material discontinuities due to weld joints, and the fatigue strength at these hot spots is very important for the structural performance. In the past, various Non-Destructive Evaluation (NDE) techniques have been developed to detect fatigue cracks and to estimate their location and size. However, an important limitation of most of the existing NDE methods is that they are off-line; the normal operation of the structure has to be interrupted and the device often has to be disassembled. In this study, a new impedance-based structural health monitoring system employing piezoceramic transducers is developed with a special interest in applying the technique for welded structural members in ship and offshore structures. In particular, the impedance-based structural health monitoring technique that employs the coupling effect of piezoceramic (PZT) materials and structures is investigated.

Electromechanical Impedance-Based Structural Health Monitoring with Evolutionary Programming

An impedance-based structural health monitoring technique is presented. By analyzing the in-plane vibration of a thin lead–zirconate–titanate (PZT) patch, the electromechanical impedance of the PZT patch is predicted. The force impedances of a beam and a plate with damage are calculated by Ritz method using polynomial as shape functions. The damage is then identified from the changes of the impedance spectra caused by the appearance of damage. A hybrid evolutionary programming is employed as a global search technique to back-calculate the damage. A specially designed fitness function is proposed, which is able to effectively reduce the inaccuracy in representing the real structure using analytical or numerical models. Experiments are carried out on a beam and a plate to verify the numerical predictions. The results demonstrate that the proposed method is able to effectively and reliably locate and quantify the damage in the beam and the plate.

Improving Accessibility of the Impedance-Based Structural Health Monitoring Method

This paper presents current research on impedance-based structural health monitoring technique. The basic principle behind this technique is to apply high-frequency structural excitations (typically higher than 30 kHz) through the 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. Three examples, including a bolted joint, gas pipeline, and composite structure, are presented to illustrate the effectiveness of this health monitoring technique to a wide variety of practical field applications. Although many proof-of-concept experiments have been performed using the impedance methods, the impedance-measuring device (HP4194A) is still bulky and expensive. Therefore, an operational amplifier-based turnkey device that can measure and record the electric impedance of a PZT has been developed. The performance of this miniaturized and portable device has been compared to our previous results and its effectiveness has been demonstrated. This paper summarizes the experimental setup, procedures, and considerations needed to implement the device in field applications.

Experimental research on evaluating structure damage with piezoelectric dynamic impedance

A dynamic impedance-based structural health monitoring technique is introduced. According to the direct and the converse piezoelectric property of piezoelectric materials, the piezoceramic ( PZT ) can be used as an actuator and a sensor synchronously. If damages like cracks, holes, debonding or loose connections are presented in the structure, the physical variations of the structure will cause the mechanical impedance modified. On the basis of introducing the principle and the theory, the experiment and the analysis on some damages of the structure are studied by means of the dynamic impedance technique. On the view of experiment, kinds of structural damages are evaluated by the information of dynamic impedance in order to validate the feasibility of the method.

Impedance-Based Structural Health Monitoring with Artificial Neural Networks

This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically >30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, multiple sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with experimental examples, investigations on a massive quarter scale model of a steel bridge section and a space truss structure, in order to verify the performance of this proposed methodology.