scholarly journals Numerical Analysis and Experimental Verification of Damage Identification Metrics for Smart Beam with MFC Elements to Support Structural Health Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6796
Author(s):  
Andrzej Koszewnik ◽  
Kacper Lesniewski ◽  
Vikram Pakrashi
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Damage Identification ◽  
The Other ◽  
Reference Structure ◽  
Damage Localization ◽  
Beam Structures ◽  
Structural Health ◽  
Piezoelectric Energy ◽  
Wide Range

This paper investigates damage identification metrics and their performance using a cantilever beam with a piezoelectric harvester for Structural Health Monitoring. In order to do this, the vibrations of three different beam structures are monitored in a controlled manner via two piezoelectric energy harvesters (PEH) located in two different positions. One of the beams is an undamaged structure recognized as reference structure, while the other two are beam structures with simulated damage in form of drilling holes. Subsequently, five different damage identification metrics for detecting damage localization and extent are investigated in this paper. Overall, each computational model has been designed on the basis of the modified First Order Shear Theory (FOST), considering an MFC element consisting homogenized materials in the piezoelectric fiber layer. Frequency response functions are established and five damage metrics are assessed, three of which are relevant for damage localization and the other two for damage extent. Experiments carried out on the lab stand for damage structure with control damage by using a modal hammer allowed to verify numerical results and values of particular damage metrics. In the effect, it is expected that the proposed method will be relevant for a wide range of application sectors, as well as useful for the evolving composite industry.

Reference-free damage localization in time-space domain for structural health monitoring of X-COR sandwich composites

2018 ◽  
Vol 30 (3) ◽  
pp. 371-385 ◽  
Author(s):  
Guoyi Li ◽  
Aditi Chattopadhyay
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Damage Localization ◽  
Sandwich Composites ◽  
Space Domain ◽  
Space Analysis ◽  
Structural Health ◽  
Time Space ◽  
Wide Range ◽  
Excitation Frequencies

This article presents a guided wave based damage localization framework using a time-space analysis for structural health monitoring of X-COR sandwich composites with a reference-free perspective to overcome the difficulty in detecting reflected guided waves in a highly attenuated media. Transducers, including macro-fiber composites and piezoelectric wafers, are used to design the sensing paths. The time-space domain is constructed using de-noised signals that are processed by signal processing techniques including matching pursuit decomposition and Hilbert transform. The localization framework is then validated across a wide range of excitation frequencies in X-COR sandwich composites with seeded facesheet delamination. The results indicate that time-space analysis offers a high accuracy for detection and localization of internal damages and serves as a promising framework for structural health monitoring of complex sandwich composites with reinforcements. This work also provides a comprehensive study of the changes in group velocities, attenuation tendencies, and time-space resolution of actuated and converted modes under different excitation frequencies across a range of ultrasonic transducer sizes, thereby helping to improve reliability and accuracy of damage localization in time-space domain.

Topology optimization of vibrational piezoelectric energy harvesters for structural health monitoring applications

2019 ◽  
Vol 30 (18-19) ◽  
pp. 2894-2907
Author(s):  
Scott Townsend ◽  
Stephen Grigg ◽  
Renato Picelli ◽  
Carol Featherston ◽  
Hyunsun Alicia Kim
Keyword(s):  
Topology Optimization ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Optimization Method ◽  
Structural Health ◽  
Energy Harvesters ◽  
Piezoelectric Energy ◽  
Piezoelectric Cantilever ◽  
Wide Range ◽  
Distinct Frequency

Aircraft structures exhibit localized vibrations over a wide range of frequencies. Such vibrations can be used to power sensors which then monitor the health of the structure. Conventional vibrational piezoelectric harvesting involves optimizing the harvester for one distinct frequency. The aim of this work is to design a wireless vibrational piezoelectric system capable of energy harvesting in the range of 100–500 Hz by tailoring the resonant behavior of cantilever structures. We herein employ a model capable of predicting the performance of a piezoelectric cantilever retrofit on a structural health monitoring sensor and then formulate a design optimization problem and solve with the level set topology optimization method. The designs are verified through fabrication of experimental prototypes.

Federal Aviation Administration’s Probability of Detection Testing Results for Structural Health Monitoring (SHM)

2021 ◽  
Author(s):  
Paul Swindell ◽  
Danielle Stephens
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Federal Aviation Administration ◽  
Steering Committee ◽  
Probability Of Detection ◽  
The Other ◽  
Atlantic City ◽  
Commercial Aviation ◽  
Structural Health ◽  
Set Up

Abstract The Federal Aviation Administration (FAA) has been participating with the Society of Automotive Engineers (SAE) Aerospace Industry Steering Committee (AISC) to develop a methodology for calculating the Probability of Detection (POD) for Structural Health Monitoring (SHM) for damage detection on commercial aviation. Two POD methodologies were developed: one by Dr. William Meeker, Iowa State University, and the other by Dennis Roach, Sandia National Laboratories (SNL). With Dr. Seth Kessler, Metis Design Corp, a test program of 24 samples of aluminum strips to be fatigued on MTS machines was developed. The samples were designed to meet the ASTM E647. Twelve samples had two SHM modalities on the front and back from Metis (PZT and carbon nanotubes), and the other twelve had SHM sensors from Structural Monitoring Systems (SMS) (comparative vacuum monitoring – CVM) and Acellent Technologies (PZT). The tests were performed at the FAA William J Hughes Technical Center in Atlantic City, NJ. The samples were cycled every 1500 cycles and then stopped for SHM data collection. Once the crack exceeded 0.125 inches and provided for a minimum of 15 inspections, a new sample was tested until all 12 samples were completed. The data was provided to each company to be set up in the format needed to run through the POD methodologies. Then the data was provided to Dr. Meeker and Dr. Roach for analysis. This paper will provide the results of those tests.

scholarly journals Kappa-PSO-FAN based method for damage identification on composite structural health monitoring

2018 ◽  
Vol 95 ◽  
pp. 1-13 ◽  
Author(s):  
Mario A. de Oliveira ◽  
Nelcileno V.S. Araujo ◽  
Daniel J. Inman ◽  
Jozue Vieira Filho
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Damage Identification ◽  
Structural Health

scholarly journals A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge

2018 ◽  
Vol 18 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Nguyen Lu Dang Khoa ◽  
Yang Wang ◽  
Bijan Samali ◽  
Xinqun Zhu
Keyword(s):  
Data Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Scale ◽  
Damage Identification ◽  
Tensor Analysis ◽  
Support Vector ◽  
Damage Scenarios ◽  
Structural Health ◽  
Cable Stayed Bridge

A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.

Feature Engineering for Structural Health Monitoring (SHM)

2021 ◽  
pp. 138-163
Author(s):  
Esraa Elhariri ◽  
Nashwa El-Bendary ◽  
Shereen A. Taie
Keyword(s):  
Computer Vision ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Damage ◽  
Emerging Technology ◽  
The Other ◽  
Feature Engineering ◽  
Future Directions ◽  
Structural Health ◽  
Computational Perspective

Feature engineering is a key component contributing to the performance of the computer vision pipeline. It is fundamental to several computer vision tasks such as object recognition, image retrieval, and image segmentation. On the other hand, the emerging technology of structural health monitoring (SHM) paved the way for spotting continuous tracking of structural damage. Damage detection and severity recognition in the structural buildings and constructions are issues of great importance as the various types of damages represent an essential indicator of building and construction durability. In this chapter, the authors connect the feature engineering with SHM processes through illustrating the concept of SHM from a computational perspective, with a focus on various types of data and feature engineering methods as well as applications and open venues for further research. Challenges to be addressed and future directions of research are presented and an extensive survey of state-of-the-art studies is also included.

Structural Health Monitoring and Damage Identification

2020 ◽  
pp. 1-72
Author(s):  
R. Fuentes ◽  
E.J. Cross ◽  
P.A. Gardner ◽  
L.A. Bull ◽  
T.J. Rogers ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Damage Identification ◽  
Structural Health
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