This work presents a detailed review of the development of distributed acoustic sensors (DAS) and their newest scientific applications. It covers most areas of human activities, such as the engineering, material, and humanitarian sciences, geophysics, culture, biology, and applied mechanics. It also provides the theoretical basis for most well-known DAS techniques and unveils the features that characterize each particular group of applications. After providing a summary of research achievements, the paper develops an initial perspective of the future work and determines the most promising DAS technologies that should be improved.

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Scientific Applications of Distributed Acoustic Sensing: State-of-the-Art Review and Perspective

A complete picture of the deformation characteristics (distribution and evolution) of the geotechnical infrastructures serves as superior information for understanding their potential instability mechanism. How to monitor more completely and accurately the deformation of these infrastructures (either artificial or natural) in the field expediently and roundly remains a scientific topic. The conventional deformation monitoring methods are mostly carried out at a limited number of discrete points and cannot acquire the deformation data of the whole structure. In this paper, a new monitoring methodology of dam deformation and associated results interpretation is presented by taking the advantages of the terrestrial laser scanning (TLS), which, in contrast with most of the conventional methods, is capable of capturing the geometric information at a huge amount of points over an object in a relatively fast manner. By employing the non-uniform rational B-splines (NURBS) technology, the high spatial resolution models of the monitored geotechnical objects can be created with sufficient accuracy based on these point cloud data obtained from application of the TLS. Finally, the characteristics of deformation, to which the geotechnical infrastructures have been subjected, are interpreted more completely according to the models created based on a series of consecutive monitoring exercises at different times. The present methodology is applied to the Changheba earth-rock dam, which allows the visualization of deformation over the entire dam during different periods. Results from analysis of the surface deformation distribution show that the surface deformations in the middle are generally larger than those on both sides near the bank, and the deformations increase with the increase of the elevations. The results from the present application highlight that the adhibition of the TLS and NURBS technology permits a better understanding of deformation behavior of geotechnical objects of large size in the field.

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Integration of Terrestrial Laser Scanning and NURBS Modeling for the Deformation Monitoring of an Earth-Rock Dam

Improving the accuracy and efficiency of damage detection of bridge structures is a major challenge in engineering practice. This paper aims to address this issue by monitoring the continuo...

Detectability of Bridge-Structural Damage Based on Fiber-Optic Sensing through Deep-Convolutional Neural Networks

Blind source extraction of acoustic emission signals for rail cracks based on ensemble empirical mode decomposition and constrained independent component analysis

Improving the accuracy and efficiency of bridge structure damage detection is one of the main challenges in engineering practice. This paper aims to address this issue by monitoring the continuous bridge deflection based on the fiber optic gyroscope and applying the deep-learning algorithm to perform structural damage detection. With a scale-down bridge model, three types of damage scenarios and an intact benchmark were simulated. A supervised learning model based on the deep convolutional neural networks was proposed. After the training process under ten-fold cross-validation, the model accuracy can reach 96.9% and significantly outperform that of other four traditional machine learning methods (random forest, support vector machine, k-nearest neighbor, and decision tree) used for comparison. Further, the proposed model illustrated its decent ability in distinguishing damage from structurally symmetrical locations.

Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection.

Real-time monitoring method for unauthorized working activities above the subway tunnel based on ultra-weak fiber Bragg grating vibration sensing array

For long-distance unattended metro tunnel monitoring systems, accurate identification of abnormal disturbances is of great significance for underground safety. Many abnormal disturbance data will be generated in the ultraweak grating sensing system along the metro tunnel. The classification and labeling of these abnormal disturbance signals is a very large and time-consuming task. Therefore, we propose an active learning method using a transductive relevance vector machine based on a Gaussian mixture model (GMM-TRVM) to classify and identify those signals. First, we analyzed the dynamic response signals of fiber Bragg grating sensors in the metro tunnel. Then, we extracted the time-domain and frequency-domain features of the abnormal disturbance signal. Taking the characteristics of abnormal disturbance signals as input and corresponding disturbance categories as output, the training of the GMM-TRVM model was completed under the active learning framework. In the circular query process, the model was evaluated after each update, and the evaluation index was assigned an ${F}_{{1}}$ score. The final results show that the proposed GMM-TRVM active learning method achieves good classification and recognition.

Classifying Tunnel Anomalies Based on Ultraweak FBGs Signal and Transductive RVM Combined With Gaussian Mixture Model

Lateral positioning of vibration source for underground pipeline monitoring based on ultra-weak fiber Bragg grating sensing array

Trajectory optimization by using EMD- and ICA-based processing method

In view of the high voltage, the strong magnetic field environment of the high-voltage switchgear, the system based on the optical wireless sensing technology, realizes the isolated contact temperature monitoring of the high-voltage switchgear. In this scheme, the good thermal conductivity and insulation ceramic materials were selected as the outer jacket material sensing probe, and the program had a good solution to the problem of high and low voltage isolation; The combination of the optical fiber composite insulators for wireless sensing, not only solves the problem of high voltage insulation, but also avoids the “creeping” phenomenon due to dust. The package of the structure and reasonable installation solves the cross sensitivity of strain. The application showed that the precision of temperature measurement of the system could reach ± 0.5 °C and could work in a variety of harsh environments. It could improve the reliability of the operation of the electrical equipment significantly and has considerable practical value for the whole power system.

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Gan Weibing

Papers

Real-time monitoring method for unauthorized working activities above the subway tunnel based on ultra-weak fiber Bragg grating vibration sensing array

Classifying Tunnel Anomalies Based on Ultraweak FBGs Signal and Transductive RVM Combined With Gaussian Mixture Model

Lateral positioning of vibration source for underground pipeline monitoring based on ultra-weak fiber Bragg grating sensing array

Trajectory optimization by using EMD- and ICA-based processing method

Study of Isolated Contact Temperature Monitoring System Based on Optical Wireless Sensing Technology