Distributed Brillouin sensor for structural health monitoring

2007 ◽  
Vol 34 (3) ◽  
pp. 291-297 ◽  
Author(s):  
Fabien Ravet ◽  
Lufan Zou ◽  
Xiaoyi Bao ◽  
Togay Ozbakkaloglu ◽  
Murat Saatcioglu ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Changes ◽  
Structural Health ◽  
Promising Tool ◽  
Reinforced Polymer ◽  
Fibre Reinforced Polymer ◽  
Structural Failures ◽  
Heavy Loads ◽  
Distributed Brillouin Sensor

The distributed Brillouin sensor (DBS) was used to monitor the structural changes in a steel pipe and a composite column subjected to heavy loads. The column was made of concrete reinforced with fibre-reinforced-polymer (FRP) rods and sheets. The test reproduced earthquake-like conditions. The pipe had a length of 2.58 m and diameter of 0.75 m. The DBS measured the strain distribution in both the concrete column and the pipe under various loads. The DBS provided detailed information on the structure's health at the local and global level, before any deformation, cracks, or buckling was visible. This work demonstrates that the DBS is capable of extracting critical information useful to engineers: the engineer's experience and judgement, in conjunction with appropriate data-processing methods, make it possible to anticipate structural failures. The DBS is a promising tool for structural health monitoring.Key words: structural health monitoring, distributed Brillouin sensor, concrete structure, pipeline buckling, strain measurement.

scholarly journals Hierarchical approach for uncertainty quantification and reliability assessment of guided wave-based structural health monitoring

2020 ◽  
pp. 147592172094064
Author(s):  
Nan Yue ◽  
M.H. Aliabadi
Keyword(s):  
Structural Health Monitoring ◽  
Carbon Fibre ◽  
Health Monitoring ◽  
Impact Damage ◽  
Guided Wave ◽  
Hierarchical Approach ◽  
Structural Health ◽  
Reinforced Polymer ◽  
Carbon Fibre Reinforced Polymer ◽  
Fibre Reinforced Polymer

In this article, a hierarchical approach is proposed for the design and assessment of a guided wave-based structural health monitoring system for the detection and localisation of barely visible impact damage in composite airframe structures. The hierarchical approach provides a systemic and practical way to establish guided wave-based structural health monitoring systems for different structures in the presence of uncertainties and to quantify system performance. The proposed approach is carried out in four steps: (1) determine optimal sensor placement for the target structure and its plausible impact scenarios, (2) set detection threshold for global damage index based on the noise level present in the required environmental and operations conditions, (3) detect damage in critical locations and quantify detection performance by calculating the probability of detection, probability of false alarm and detection accuracy and (4) locate the detected damage while also quantifying the accuracy of location estimation and the probability of correctly indicating if the damage is in an area critical to the integrity of the structure. The proposed approach is demonstrated in aircraft carbon fibre-reinforced polymer structures from coupon level (simple flat panels) to sub-component level (large flat panel with multiple carbon fibre-reinforced polymer stringers and aluminium frames) for the detection and localisation of barely visible impact damage.

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

scholarly journals Vibration-Based Structural Health Monitoring Using Piezoelectric Transducers and Parametric t-SNE

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1716
Author(s):  
David Agis ◽  
Francesc Pozo
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Changes ◽  
Computational Cost ◽  
Principal Component ◽  
Piezoelectric Transducers ◽  
Majority Voting ◽  
Structural Health

In this paper, we evaluate the performance of the so-called parametric t-distributed stochastic neighbor embedding (P-t-SNE), comparing it to the performance of the t-SNE, the non-parametric version. The methodology used in this study is introduced for the detection and classification of structural changes in the field of structural health monitoring. This method is based on the combination of principal component analysis (PCA) and P-t-SNE, and it is applied to an experimental case study of an aluminum plate with four piezoelectric transducers. The basic steps of the detection and classification process are: (i) the raw data are scaled using mean-centered group scaling and then PCA is applied to reduce its dimensionality; (ii) P-t-SNE is applied to represent the scaled and reduced data as 2-dimensional points, defining a cluster for each structural state; and (iii) the current structure to be diagnosed is associated with a cluster employing two strategies: (a) majority voting; and (b) the sum of the inverse distances. The results in the frequency domain manifest the strong performance of P-t-SNE, which is comparable to the performance of t-SNE but outperforms t-SNE in terms of computational cost and runtime. When the method is based on P-t-SNE, the overall accuracy fluctuates between 99.5% and 99.75%.

Lamb Waves Signal Analysis in Structural Health Monitoring Systems

2011 ◽  
Vol 368-373 ◽  
pp. 2402-2405
Author(s):  
Nai Zhi Zhao ◽  
Chang Tie Huang ◽  
Xin Chen
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Changes ◽  
Lamb Waves ◽  
Detection Algorithm ◽  
Operational Conditions ◽  
Structural Health ◽  
Detection Algorithms ◽  
Baseline Subtraction

Many of the wave propagation based structural health monitoring techniques rely on some knowledge of the structure in a healthy state in order to identify damage. Baseline measurements are recorded when a structure is pristine and are stored for comparison to future data. A concern with the use of baseline subtraction methods is the ability to discern structural changes from the effects of varying environmental and operational conditions when analyzing the vibration response of a system. The use of a standard baseline subtraction technique may falsely indicate damage when environmental or operational variations are present between baseline measurements and new measurements. A procedure was outlined for the method, including excitation and recording of Lamb waves, and the use of damage detection algorithms. In this paper, several tests are performed and the results are used to help develop the damage detection algorithms previously described, and to evaluate the performance of the instantaneous baseline SHM technique. Analytical testing is first performed by feeding known input signals into each damage detection algorithm and analyzing the output data. The results of the analytical testing are used to help develop the damage detection algorithms.

SHM System Based on Modal Filtration

2012 ◽  
Vol 518 ◽  
pp. 289-297 ◽  
Author(s):  
Krzysztof Mendrok ◽  
Tadeusz Uhl ◽  
Wojciech Maj ◽  
Paweł Paćko
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Modification ◽  
Structural Changes ◽  
Environmental Changes ◽  
Practical Implementation ◽  
Health Monitoring System ◽  
Structural Health ◽  
Changes Detection

The modal filter has various applications, among the others for damage detection. It was shown, that a structural modification (e.g. drop of stiffness due to a crack) causes an appearance of peaks on the output of the modal filter. This peaks result from not perfect modal filtration due to system local structural changes. That makes it a great indicator for damage detection, which has fallowing advantages: low computational afford due to the data reduction, the structural health monitoring system based on it, is easy to automate. Furthermore the system is theoretically insensitive to environmental changes as temperature or humidity variation (global structural changes do not cause a drop of modal filtration accuracy). In the paper the practical implementation of the presented technique is shown. The developed structural health monitoring (SHM) system is described as well as results of its extensive simulation and laboratory testing. Finally the application of the system for the structural changes detection on the airplane parts is presented..

Computational Setup of Structural Health Monitoring for Real-Time Thermal Verification

2011 ◽  
Author(s):  
Derek Doyle ◽  
Whitney Reynolds ◽  
Brandon Arritt ◽  
Brenton Taft
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Changes ◽  
Space Vehicles ◽  
Lap Joint ◽  
Active Sensors ◽  
Structural Health ◽  
Piezoelectric Wafer Active Sensors ◽  
Piezoelectric Wafer ◽  
Inspection Techniques

Research at the AFRL Space Vehicles Directorate is being conducted to reduce schedule times for assembly, integration, and test, to make satellite-based capabilities more responsive to user needs. Structural Health Monitoring has been pursued as a means for validating workmanship and has been proven on PnPSat-1. Embedded ultrasonic piezoelectric wafer active sensors (PWAS) have been utilized with local and global inspection techniques, developed both in house and by collaborating universities, to detect structural changes that may occur during assembly, integration, and test. Specific attention has focused on interface qualification. It is now reasonable to believe that evaluation of interfaces through the use of such sensors can also be used to indirectly qualify the structure thermally and that tedious thermal-vacuum testing may be truncated or eliminated altogether. This paper focuses on the computational development of extracting thermal properties from ultrasonic transmission records. Methods are validated on simple bolted lap-joint cantilever beams.

scholarly journals Assessment of embedded fiber Bragg gratings for structural health monitoring of composites

2016 ◽  
Vol 16 (3) ◽  
pp. 262-275 ◽  
Author(s):  
Mike Yeager ◽  
Michael Todd ◽  
William Gregory ◽  
Chris Key
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Composite Structures ◽  
Fiber Bragg Gratings ◽  
Bragg Gratings ◽  
Fiber Reinforced Polymer ◽  
System Level ◽  
Fiber Reinforced ◽  
Structural Health ◽  
Reinforced Polymer

This work provides a system-level investigation into the use of embedded fiber Bragg grating optical sensors as a viable sensing architecture for the structural health monitoring of composite structures. The practical aspects of the embedding process are documented for both carbon fiber–reinforced polymer and glass fiber–reinforced polymer structures manufactured by both oven vacuum bag and vacuum-assisted resin transfer method processes. Initially, embedded specimens were subject to long-term water submersion to verify performance in an underwater environment. A larger, more complex jointed specimen was also fabricated with a fully embedded sensor network of fiber Bragg gratings and subjected to incrementally induced bearing damage. Using commercially available interrogation hardware, a damage detection structural health monitoring algorithm was developed and deployed. The results permit statistically precise detection of low levels of connection damage in the composite specimen.

Effects of environmental and operational variability on structural health monitoring

2006 ◽  
Vol 365 (1851) ◽  
pp. 539-560 ◽  
Author(s):  
Hoon Sohn
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Changes ◽  
Vibration Signal ◽  
Data Normalization ◽  
Operational Conditions ◽  
Structural Health ◽  
Structural Deterioration ◽  
Damage Data ◽  
Environmental Variations

Stated in its most basic form, the objective of structural health monitoring is to ascertain if damage is present or not based on measured dynamic or static characteristics of a system to be monitored. In reality, structures are subject to changing environmental and operational conditions that affect measured signals, and these ambient variations of the system can often mask subtle changes in the system's vibration signal caused by damage. Data normalization is a procedure to normalize datasets, so that signal changes caused by operational and environmental variations of the system can be separated from structural changes of interest, such as structural deterioration or degradation. This paper first reviews the effects of environmental and operational variations on real structures as reported in the literature. Then, this paper presents research progresses that have been made in the area of data normalization.

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