scholarly journals Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6844
Author(s):  
Luna Ngeljaratan ◽  
Mohamed A. Moustafa
Keyword(s):  
Structural Health Monitoring ◽  
Image Compression ◽  
Health Monitoring ◽  
Measurement Accuracy ◽  
Small Scale ◽  
Response Measurement ◽  
Static Test ◽  
Structural Health ◽  
Close Range Photogrammetry ◽  
Close Range

Much research is still underway to achieve long-term and real-time monitoring using data from vision-based sensors. A major challenge is handling and processing enormous amount of data and images for either image storage, data transfer, or image analysis. To help address this challenge, this study explores and proposes image compression techniques using non-adaptive linear interpolation and wavelet transform algorithms. The effect and implication of image compression are investigated in the close-range photogrammetry as well as in realistic structural health monitoring applications. For this purpose, images and results from three different laboratory experiments and three different structures are utilized. The first experiment uses optical targets attached to a sliding bar that is displaced by a standard one-inch steel block. The effect of image compression in the photogrammetry is discussed and the monitoring accuracy is assessed by comparing the one-inch value with the measurement from the optical targets. The second application is a continuous static test of a small-scale rigid structure, and the last application is from a seismic shake table test of a full-scale 3-story building tested at E-Defense in Japan. These tests aimed at assessing the static and dynamic response measurement accuracy of vision-based sensors when images are highly compressed. The results show successful and promising application of image compression for photogrammetry and structural health monitoring. The study also identifies best methods and algorithms where effective compression ratios up to 20 times, with respect to original data size, can be applied and still maintain displacement measurement accuracy.

scholarly journals Underexposed Vision-Based Sensors’ Image Enhancement for Feature Identification in Close-Range Photogrammetry and Structural Health Monitoring

2021 ◽  
Vol 11 (23) ◽  
pp. 11086
Author(s):  
Luna Ngeljaratan ◽  
Mohamed A. Moustafa
Keyword(s):  
Structural Health Monitoring ◽  
Image Enhancement ◽  
Health Monitoring ◽  
Bundle Adjustment ◽  
Practical Implementation ◽  
Identification Algorithm ◽  
Feature Identification ◽  
Structural Health ◽  
Close Range Photogrammetry ◽  
Close Range

This paper describes an alternative structural health monitoring (SHM) framework for low-light settings or dark environments using underexposed images from vision-based sensors based on the practical implementation of image enhancement algorithms. The proposed framework was validated by two experimental works monitored by two vision systems under ambient lights without assistance from additional lightings. The first experiment monitored six artificial templates attached to a sliding bar that was displaced by a standard one-inch steel block. The effect of image enhancement in the feature identification and bundle adjustment integrated into the close-range photogrammetry were evaluated. The second validation was from a seismic shake table test of a full-scale three-story building tested at E-Defense in Japan. Overall, this study demonstrated the efficiency and robustness of the proposed image enhancement framework in (i) modifying the original image characteristics so the feature identification algorithm is capable of accurately detecting, locating and registering the existing features on the object; (ii) integrating the identified features into the automatic bundle adjustment in the close-range photogrammetry process; and (iii) assessing the measurement of identified features in static and dynamic SHM, and in structural system identification, with high accuracy.

Application of close range photogrammetry in structural health monitoring by processing generated point cloud datasets

2021 ◽  
Author(s):  
Ali Mirzazade ◽  
Cosmin Popescu ◽  
Thomas Blanksvärd ◽  
Björn Täljsten
Keyword(s):  
Health Monitoring ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Optical Data ◽  
Small Scale ◽  
Relevant Parameter ◽  
Bridge Inspection ◽  
Close Range Photogrammetry ◽  
Close Range

<p>In bridge inspection, vertical displacement is a relevant parameter for both short and long-term health monitoring. Assessing change in deflections could also simplify the assessment work for inspectors. Recent developments in digital camera technology and photogrammetry software enables point cloud with colour information (RGB values) to be generated. Thus, close range photogrammetry offers the potential of monitoring big and small-scale damages by point clouds. The current paper aims to monitor geometrical deviations in Pahtajokk Bridge, Northern Sweden, using an optical data acquisition technique. The bridge in this study is scanned two times by almost one year a part. After point cloud generation the datasets were compared to detect geometrical deviations. First scanning was carried out by both close range photogrammetry (CRP) and terrestrial laser scanning (TLS), while second scanning was performed by CRP only. Analyzing the results has shown the potential of CRP in bridge inspection.</p>

scholarly journals HIGHWAY INFRASTRUCTURE HEALTH MONITORING USING MICRO-ELECTROMECHANICAL SENSORS AND SYSTEMS (MEMS)

2014 ◽  
Vol 19 (Supplement_1) ◽  
pp. S188-S201 ◽  
Author(s):  
Halil Ceylan ◽  
Kasthurirangan Gopalakrishnan ◽  
Sunghwan Kim ◽  
Peter C. Taylor ◽  
Maxim Prokudin ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Radio Frequency Identification ◽  
Life Cycle Cost ◽  
Construction Material ◽  
Small Scale ◽  
Mems Devices ◽  
Infrastructure Systems ◽  
Highway Infrastructure ◽  
Structural Health

The development of novel “smart” structures by embedding sensing capabilities directly into the construction material during the manufacturing and deployment process has attracted significant attention in autonomous structural health monitoring (SHM). Micro-electromechanical systems (MEMS) provide vast improvements over existing sensing methods in the context of SHM of highway infrastructure systems, including improved system reliability, improved longevity and enhanced system performance, improved safety against natural hazards and vibrations, and a reduction in life cycle cost in both operating and maintaining the infrastructure. Advancements in MEMS technology and wireless sensor networks provide opportunities for long-term, continuous, real-time structural health monitoring of pavements and bridges at low cost within the context of sustainable infrastructure systems. Based on a comprehensive review of literature and vendor survey, the latest information available on off-the-shelf MEMS devices, as well as research prototypes, for bridge, pavement, and traffic applications are synthesized in this paper. In addition, the paper discusses the results of a laboratory study as well as a small-scale field study on the use of a wireless concrete monitoring system based on radio-frequency identification (RFID) technology and off-the-shelf MEMS-based temperature and humidity sensors.

scholarly journals Structural Health Monitoring for Jacket-Type Offshore Wind Turbines: Experimental Proof of Concept

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1835 ◽  
Author(s):  
Yolanda Vidal ◽  
Gabriela Aquino ◽  
Francesc Pozo ◽  
José Eligio Moisés Gutiérrez-Arias
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbines ◽  
Health Monitoring ◽  
Vibration Response ◽  
Offshore Wind ◽  
Support Vector ◽  
Soil Conditions ◽  
Small Scale ◽  
Offshore Wind Turbines ◽  
Structural Health

Structural health monitoring for offshore wind turbines is imperative. Offshore wind energy is progressively attained at greater water depths, beyond 30 m, where jacket foundations are presently the best solution to cope with the harsh environment (extreme sites with poor soil conditions). Structural integrity is of key importance in these underwater structures. In this work, a methodology for the diagnosis of structural damage in jacket-type foundations is stated. The method is based on the criterion that any damage or structural change produces variations in the vibrational response of the structure. Most studies in this area are, primarily, focused on the case of measurable input excitation and vibration response signals. Nevertheless, in this paper it is assumed that the only available excitation, the wind, is not measurable. Therefore, using vibration-response-only accelerometer information, a data-driven approach is developed following the next steps: (i) the wind is simulated as a Gaussian white noise and the accelerometer data are collected; (ii) the data are pre-processed using group-reshape and column-scaling; (iii) principal component analysis is used for both linear dimensionality reduction and feature extraction; finally, (iv) two different machine-learning algorithms, k nearest neighbor (k-NN) and quadratic-kernel support vector machine (SVM), are tested as classifiers. The overall accuracy is estimated by 5-fold cross-validation. The proposed approach is experimentally validated in a laboratory small-scale structure. The results manifest the reliability of the stated fault diagnosis method being the best performance given by the SVM classifier.

scholarly journals Bayesian Computational Sensor Networks: Small-scale Structural Health Monitoring

2015 ◽  
Vol 51 ◽  
pp. 2603-2612 ◽  
Author(s):  
Wenyi Wang ◽  
Anshul Joshi ◽  
Nishith Tirpankar ◽  
Philip Erickson ◽  
Michael Cline ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Small Scale ◽  
Structural Health

Fatigue damage detection from imbalanced inspection data of Lamb wave

2021 ◽  
pp. 147592172110152
Author(s):  
Jingjing He ◽  
Ziwei Fang ◽  
Jie Liu ◽  
Fei Gao ◽  
Jing Lin
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Lamb Wave ◽  
Sampling Technique ◽  
Support Vector ◽  
Small Scale ◽  
Detection Range ◽  
Structural Health ◽  
Inspection Data

The core of structural health monitoring is to provide a real-time monitoring, inspection, and damage detection of structures. As one of the most promising technology to structural health monitoring, the Lamb wave method has attracted interest because it is sensitive to small-scale damage with a long detection range. However, in many real-world structural health monitoring applications, the nature of the problem implies structures work under normal condition in most of its operating phases; therefore, classes of data collected are not equally represented. The predictive capability of damage detection algorithms may significantly be impaired by class imbalance. This article presents a damage detection method using imbalanced inspection data which is collected through Lamb wave detection. Aiming at maximizing detection accuracy, an improved synthetic minority over-sampling technique using three-point triangle (triangle synthetic minority over-sampling technique) is proposed to conduct the over-sampling procedure and increase the number of minority samples. The iterative-partitioning filter is employed to remove the noisy examples which may be introduced by triangle synthetic minority over-sampling technique. Three conventional classification methods, namely, support vector machine, decision tree, and k-nearest neighbor, are used to perform the damage detection. A fatigue crack detection test using Lamb wave is performed to demonstrate the overall procedure of the proposed method. Three damage sensitive features, namely, normalized amplitude, correlation coefficient, and normalized energy, are extracted from signals as datasets. A cross-validation is performed to verify the performance of the proposed method for crack size identification.

Recent advancements and vision toward stretchable bio-inspired networks for intelligent structures

2014 ◽  
Vol 13 (6) ◽  
pp. 609-620 ◽  
Author(s):  
Nathan Salowitz ◽  
Zhiqiang Guo ◽  
Surajit Roy ◽  
Raphael Nardari ◽  
Yu-Hung Li ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Hot Spot ◽  
Complementary Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Small Scale ◽  
Integrated Networks ◽  
Structural Health ◽  
Ongoing Work ◽  
Parasitic Effects

Significant progress has recently been achieved in structural health monitoring, maturing the technology through quantification, validation, and verification to promote implementation and fielding of SHM. In addition, there is ongoing work seeking to detect damage precursors and to deploy structural health monitoring systems over large areas, moving the technology beyond hot-spot monitoring to global state sensing for full structural coverage. A large number of small sensors of multiple types are necessary in order to accomplish the goals of structural health monitoring, enabling increased sensing capabilities while reducing parasitic effects on host structures. Conventional sensors are large and heavy, adding to the weight of a structure and requiring physical accommodation without adding to and potentially degrading the strength of the overall structure. Increased numbers of sensors must also be deployed to span large areas while maintaining or increasing sensing resolution and capabilities. Traditionally, these sensors are assembled, wired, and installed individually, by hand, making mass deployment prohibitively time consuming and expensive. In order to overcome these limitations, the Structures and Composites Lab at Stanford University has worked to develop bio-inspired microfabricated stretchable sensor networks. Adopting the techniques of complementary metal-oxide semiconductor and microelectromechanical system fabrication, new methods are being developed to create integrated networks of large numbers of various micro-scale sensors, processors, switches, and all wiring in a single fabrication process. Then the networks are stretched to span areas orders of magnitude larger than the original fabrication area and deployed onto host structures. The small-scale components enable interlaminar installation in laminar composites or adhesive layers of built-up structures while simultaneously minimizing parasitic effects on the host structure. Additionally, data processing and interpretation capabilities could be embedded into the network before material integration to make the material truly multifunctional and intelligent once fully deployed. This article reviews the current accomplishments and future vision for these systems in the pursuit of state sensing and intelligent materials for self-diagnostics and health monitoring.

A Novel Structural Health Monitoring System Using Surface Acoustic Sensor Network and Guided Lamb Waves

2019 ◽  
Author(s):  
Prabhav Borate ◽  
Azam Thatte
Keyword(s):  
Structural Health Monitoring ◽  
Diagnostic Imaging ◽  
Monitoring System ◽  
Health Monitoring ◽  
Lamb Waves ◽  
Small Scale ◽  
Impeller Blade ◽  
Health Monitoring System ◽  
Structural Health ◽  
Structural Health Monitoring System

Abstract This paper focuses on the development of a structural health monitoring system based on guided Lamb waves propagating over the structure and a network of surface acoustic sensors in communication at high frequencies. A time-of-flight (ToF), algorithm and a probabilistic diagnostic imaging and calibration method is developed to detect miniscule material losses or material adhesion as well as the defects like small scale holes and cracks in turbomachinery components like blades, rotors, plates and pipes. Using an advanced ToF algorithm, precise differences in timescales for arrival of symmetric / antisymmetric lamb wave packets are found for all possible combinations of actuator-sensor pairs. This leads to a deterministic mathematical construct for damage localization for various actuator-sensor pairs at focal points. In the probabilistic diagnostic imaging (PDI) method, field value is assigned based on fusion of wave signals rendered by various actuator-sensor paths to indicate the probability of the presence of a damage at a particular location on the structure. Correlation coefficients between healthy and damaged data for each of the actuator-sensor path is used to calculate the field value for each pixel on the structure. Damage calibration curve is developed by progressively increasing the damage and obtaining a magnitude of the probability density function of the severity of the damage. Proposed approach has been validated using experimental data for multiple damage cases on plates, internal surfaces of pipes and impeller blade to successfully detect submillimeter scale holes and cracks, material adhesion as well as rate of pipeline erosion and corrosion.

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