• Smart Structures and Systems
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• v.15 no.2
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• pp.489-503
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• 2015

Response estimation at unmeasured locations using the limited number of measurements is an attractive topic in the field of structural health monitoring (SHM). Because of increasing complexity and size of civil engineering structures, measuring all structural responses from the entire body is intractable for the SHM purpose; the response estimation can be an effective and practical alternative. This paper investigates a response estimation technique based on the Kalman state estimator to combine multi-sensor data under non-zero mean input excitations. The Kalman state estimator, constructed based on the finite element (FE) model of a structure, can efficiently fuse different types of data of acceleration, strain, and tilt responses, minimizing the intrinsic measurement noise. This study focuses on the effects of (a) FE model error and (b) combinations of multi-sensor data on the estimation accuracy in the case of non-zero mean input excitations. The FE model error is purposefully introduced for more realistic performance evaluation of the response estimation using the Kalman state estimator. In addition, four types of measurement combinations are explored in the response estimation: strain only, acceleration only, acceleration and strain, and acceleration and tilt. The performance of the response estimation approach is verified by numerical and experimental tests on a simply-supported beam, showing that it can successfully estimate strain responses at unmeasured locations with the highest performance in the combination of acceleration and tilt.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.749-765
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• 2010

There are on-going efforts to utilize guided waves for structural damage detection. Active sensing devices such as lead zirconate titanate (PZT) have been widely used for guided wave generation and sensing. In addition, there has been increasing interest in adopting wireless sensing to structural health monitoring (SHM) applications. One of major challenges in wireless SHM is to secure power necessary to operate the wireless sensors. However, because active sensing devices demand relatively high electric power compared to conventional passive sensors such as accelerometers and strain gauges, existing battery technologies may not be suitable for long-term operation of the active sensing devices. To tackle this problem, a new wireless power transmission paradigm has been developed in this study. The proposed technique wirelessly transmits power necessary for PZT-based guided wave generation using laser and optoelectronic devices. First, a desired waveform is generated and the intensity of the laser source is modulated accordingly using an electro-optic modulator (EOM). Next, the modulated laser is wirelessly transmitted to a photodiode connected to a PZT. Then, the photodiode converts the transmitted light into an electric signal and excites the PZT to generate guided waves on the structure where the PZT is attached to. Finally, the corresponding response from the sensing PZT is measured. The feasibility of the proposed method for wireless guided wave generation has been experimentally demonstrated.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.4
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• pp.281-287
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• 2016

Fiber Bragg grating (FBG) sensors are applied in structural health monitoring (SHM) in various application fields because of their ease of multiplexing and capability of performing absolute measurements. Moreover, the packaging methods of FBG sensors accelerate their commercialization rapidly. However, long-term SHM exposes the FBG sensors to cyclic thermal loads, and a investigation is required because it finally leads to the signal instability of the FBG sensors. In this study, the effects of sensor packaging methods two methods are generally used for the FBGs: (bonding both sides of the FBG or bonding the FBG directly on signal stability of FBG sensors are investigated. Tests are conducted on specimens in a thermal chamber, over a temperature range from $-20^{\circ}C$ to $60^{\circ}C$ for 300 cycles. Signal characteristics such as Bragg wavelength, light intensity and full width at half maximum are examined and are compared with those of the FBG sensors, obtained in a previous study under direct bonding conditions. From the comparison, it is observed that the FBG sensors with bonding on both sides of the FBG demonstrate higher signal stabilities when exposed to cyclic thermal loads during long-term SHM. Consequently, it guarantees more effectiveness when packaging the FBG sensors.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.3
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• pp.211-216
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• 2016

In several industrial fields, epoxy resin is widely used as an adhesive for co-curing and manufacturing various structures. Controlling the manufacturing process is required for ensuring robust bonding performance and the stability of the structures. A fiber optic sensor is suitable for the cure monitoring of epoxy resin owing to the thready shape of the sensor. In this paper, a fiber Bragg grating (FBG) sensor was applied for the cure monitoring of epoxy resin. Based on the experimental results, it was demonstrated that the FBG sensor can monitor the status of epoxy resin curing by measuring the strain caused by volume shrinkage and considering the compensation of temperature. In addition, two types of epoxy resin were used for the cure-monitoring; moreover, when compared to each other, it was found that the two types of epoxy had different cure-processes in terms of the change of strain during the curing. Therefore, the study proved that the FBG sensor is very profitable for the cure-monitoring of epoxy resin.

• Smart Structures and Systems
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• v.9 no.5
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• pp.461-472
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• 2012

This study proposes an optics-based active sensing system for continuous monitoring of underground pipelines in nuclear power plants (NPPs). The proposed system generates and measures guided waves using a single laser source and optical cables. First, a tunable laser is used as a common power source for guided wave generation and sensing. This source laser beam is transmitted through an optical fiber, and the fiber is split into two. One of them is used to actuate macro fiber composite (MFC) transducers for guided wave generation, and the other optical fiber is used with fiber Bragg grating (FBG) sensors to measure guided wave responses. The MFC transducers placed along a circumferential direction of a pipe at one end generate longitudinal and flexural modes, and the corresponding responses are measured using FBG sensors instrumented in the same configuration at the other end. The generated guided waves interact with a defect, and this interaction causes changes in response signals. Then, a damage-sensitive feature is extracted from the response signals using the axi-symmetry nature of the measured pitch-catch signals. The feasibility of the proposed system has been examined through a laboratory experiment.

• Smart Structures and Systems
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• v.29 no.1
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• pp.207-220
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• 2022

Structural health monitoring (SHM) plays a vital role in the maintenance and operation of constructions. In recent years, autonomous inspection has received considerable attention because conventional monitoring methods are inefficient and expensive to some extent. To develop autonomous inspection, a potential approach of crack identification is needed to locate defects. Therefore, this study exploits two deep learning-based segmentation models, DeepLabv3+ and Mask R-CNN, for crack segmentation because these two segmentation models can outperform other similar models on public datasets. Additionally, impacts of label quality on model performance are explored to obtain an empirical guideline on the preparation of image datasets. The influence of image cropping and label refining are also investigated, and different strategies are applied to the dataset, resulting in six alternated datasets. By conducting experiments with these datasets, the highest mean Intersection-over-Union (mIoU), 75%, is achieved by Mask R-CNN. The rise in the percentage of annotations by image cropping improves model performance while the label refining has opposite effects on the two models. As the label refining results in fewer error annotations of cracks, this modification enhances the performance of DeepLabv3+. Instead, the performance of Mask R-CNN decreases because fragmented annotations may mistake an instance as multiple instances. To sum up, both DeepLabv3+ and Mask R-CNN are capable of crack identification, and an empirical guideline on the data preparation is presented to strengthen identification successfulness via image cropping and label refining.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.5
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• pp.362-368
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• 2014

Fiber optic sensors (FOS) have advantages such as electromagnetic interference (EMI) immunity, corrosion resistance and multiplexing capability. For these reasons, they are widely used in various condition monitoring systems (CMS). This study investigated a muscular condition monitoring system using fiber optic sensors (FOS). Generally, sensors for monitoring the condition of the human body are based on electro-magnetic devices. However, such an electrical system has several weaknesses, including the potential for electro-magnetic interference and distortion. Fiber Bragg grating (FBG) sensors overcome these weaknesses, along with simplifying the devices and increasing user convenience. To measure the level of muscle contraction and relaxation, which indicates the musle condition, a belt-shaped FBG sensor module that makes it possible to monitor the movement of muscles in the radial and circumferential directions was fabricated in this study. In addition, a uniaxial tensile test was carried out in order to evaluate the applicability of this FBG sensor module. Based on the experimental results, a relationship was observed between the tensile stress and Bragg wavelength of the FBG sensors, which revealed the possibility of fabricating a muscular condition monitoring system based on FBG sensors.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.7
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• pp.613-620
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• 2017

A metal-coated FBG (fiber Bragg grating) sensor has a memory effect, which can recall the maximum strains experienced by the structure. In this study, a nickel-coated FBG sensor was fabricated through electroless (i.e., chemical plating) and electroplating. A thickness of approximately $43{\mu}m$ of a nickel layer was achieved. Then, we conducted cyclic loading tests for the fabricated nickel-coated FBG sensors to verify their capability to produce residual strains. The results revealed that the residual strain induced by the nickel coating linearly increased with an increase in the maximum strain experienced by the sensor. Therefore, we verified that a nickel-coated FBG sensor has a memory effect. The fabrication methods and the results of the cycle loading test will provide basic information and guidelines in the design of a nickel-coated FBG sensor when it is applied in the development of structural health monitoring techniques.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.2
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• pp.93-101
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• 2016

This paper presents the results obtained using time-series-based methods for structural damage assessment. The methods are applied to a wind turbine blade structure subjected to fatigue loads. A 9 m CX-100 (carbon experimental 100 kW) blade is harmonically excited at its first natural frequency to introduce a failure mode. Consequently, a through-thickness fatigue crack is visually identified at 8.5 million cycles. The time domain data from the piezoelectric active-sensing techniques are measured during the fatigue loadings and used to detect incipient damage. The damage-sensitive features, such as the first four moments and a normality indicator, are extracted from the time domain data. Time series autoregressive models with exogenous inputs are also implemented. These features could efficiently detect a fatigue crack and are less sensitive to operational variations than the other methods.