Design and Implementation of an Accurate, Portable, and Time-Efficient Impedance-Based Transceiver for Structural Health Monitoring

Hoshyarmanesh, Hamidreza; Abbasi, Ali; Moein, Peyman; Ghodsi, Mojtaba; Zareinia, Kourosh

doi:10.1109/tmech.2017.2761902

Cited by 20 publications

(22 citation statements)

References 14 publications

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“…In this context, the developments such as new transducers and low-cost sensor networks for inspecting large structures, together with the benefits gained by these low-cost sensors compared with the cost of visual supervision and traditional inspection methods in applications such as the inspection of civil structures [40], have resulted in the rapid expansion of the use of SHM. As stated previously, this expansion has been mainly driven by the economic advantages of its use and its fast implementation [41], with the clear and direct consequence of money saved in the long term.…”

Section: Economic Justificationmentioning

confidence: 99%

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Burgos

Vargas

Pedraza

et al. 2020

Sensors

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The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.

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Section: Economic Justificationmentioning

confidence: 99%

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Burgos

Vargas

Pedraza

et al. 2020

Sensors

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“…Table II summarises the results using the same metrics as the simulation. Displacements measured with the FMCW system were treated as IDDs, and then converted to IDRs using Equation (8). Values for h i and l were selected such that the maximum IDR was equal to 0.015 for ease of comparison with the simulated system.…”

Section: Experimental Validationmentioning

confidence: 99%

“…There are currently numerous existing types of SHM sensors. These sensors include impedance-measuring devices [8], accelerometers [9], [10], and sensors which measure displacement directly, such as non-contact line scanners [11], [12], [13] and directly-connected linear variable differential transformer (LVDT) methods. The former requires direct line of sight (LOS) between both ends of the measured distance, and the latter requires space in the structure for the system to be installed and can be damaged.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of a Radar-Based Structural Health Monitoring System

Amies

Pretty

Rodgers

et al. 2019

IEEE/ASME Trans. Mechatron.

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This paper presents a new structural health monitoring (SHM) method using frequency-modulated continuous wave (FMCW) radar. The method was developed to circumvent issues with SHM methods' need for displacement measurements, which can be difficult to obtain robustly through integrated accelerations, or through other displacement measurement methods. Instead, interstorey drift ratios were estimated through the direct measurement of interstorey displacement using FMCW radar. Simulation of this method using historical structural response data verified suitably accurate displacement measurements could be obtained using FMCW radar, and prompted the construction of a prototype system. Experimental validation of this prototype was carried out on a shake table. The precision of the system in terms of mean interstorey drift ratio was found to be 1.09 × 10 −3. These results are encouraging for the future deployment of this SHM approach.

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“…They used both electromechanical impedances and guided ultrasonic waves signals, which were obtained from an embedded piezoelectric sensor module. In this sense, Hoshyarmanesh et al [ 20 ] proposed a low-cost, compact, and portable transceiver for exploring periodic structural health monitoring of a rotary structure using the EMI technique. The authors highlighted that the compactness of the proposed system is an essential requirement for rotary structures when compared with bulky, heavy, and expensive impedance analyzers.…”

Section: Introductionmentioning

confidence: 99%

Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors

Oliveira¹,

Araújo

Silva³

et al. 2018

Sensors

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A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI) technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1) Savitzky–Golay (SG) filter, using both first and second derivatives; (2) Probabilistic Neural Network (PNN); and, (3) Simplified Fuzzy ARTMAP Network (SFAN). Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate) patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario.

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Design and Implementation of an Accurate, Portable, and Time-Efficient Impedance-Based Transceiver for Structural Health Monitoring

Cited by 20 publications

References 14 publications

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Damage Identification in Structural Health Monitoring: A Brief Review from its Implementation to the Use of Data-Driven Applications

Experimental Validation of a Radar-Based Structural Health Monitoring System

Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors

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