Nonlinear ultrasonic detection for evaluating fatigue crack in metal plate

2018 ◽  
Vol 18 (3) ◽  
pp. 869-881 ◽  
Author(s):  
Rong Wang ◽  
Qi Wu ◽  
Fengming Yu ◽  
Yoji Okabe ◽  
Ke Xiong
Keyword(s):  
Fatigue Crack ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Lamb Wave ◽  
Fatigue Cracks ◽  
Crack Model ◽  
Monitoring Method ◽  
Cycle Number ◽  
Structural Health ◽  
Nonlinear Ultrasonic

In engineering structures, metal materials always endure fatigue cracks under long-term service. There has been a demand for developing a structural health monitoring method to evaluate micro-sized fatigue cracks, as cracking is considered as a precursor to structural failure. However, conventional linear-ultrasound-based technology is not sensitive to crack when it is barely visible in a metal medium. In this article, we present a nonlinear ultrasonic technology based on crack–wave interaction to investigate the growth of a fatigue crack. A breathing-crack model with a plastic zone around it was precisely established to reveal the change in the Lamb wave. The relative nonlinear parameter calculated from the fundamental and harmonic components of the Lamb wave showed linearly increasing with the growth of the fatigue crack. The relative nonlinearity was related to ultrasonic parameters, such as the cycle number and the excited frequency of the tone-burst signal. In addition, it was also related to the angle between the sensor and the crack rather than their distance. A set of experiments were conducted, demonstrating that the increasing trend of ultrasonic nonlinearity fits very well to the finite element analysis results. In conclusion, the nonlinear ultrasonic method that can be applied to the detection of micro fatigue cracks in metal plates is an effective structural health monitoring technique.

scholarly journals Guided Wave-Convolutional Neural Network Based Fatigue Crack Diagnosis of Aircraft Structures

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3567 ◽  
Author(s):  
Xu ◽  
Yuan ◽  
Chen ◽  
Ren
Keyword(s):  
Neural Network ◽  
Fatigue Crack ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Health Monitoring ◽  
Guided Wave ◽  
Engineering Applications ◽  
Monitoring Method ◽  
Structural Health ◽  
High Level

Fatigue crack diagnosis (FCD) is of great significance for ensuring safe operation, prolonging service time and reducing maintenance cost in aircrafts and many other safety-critical systems. As a promising method, the guided wave (GW)-based structural health monitoring method has been widely investigated for FCD. However, reliable FCD still meets challenges, because uncertainties in real engineering applications usually cause serious change both to the crack propagation itself and GW monitoring signals. As one of deep learning methods, convolutional neural network (CNN) owns the ability of fusing a large amount of data, extracting high-level feature expressions related to classification, which provides a potential new technology to be applied in the GW-structural health monitoring method for crack evaluation. To address the influence of dispersion on reliable FCD, in this paper, a GW-CNN based FCD method is proposed. In this method, multiple damage indexes (DIs) from multiple GW exciting-acquisition channels are extracted. A CNN is designed and trained to further extract high-level features from the multiple DIs and implement feature fusion for crack evaluation. Fatigue tests on a typical kind of aircraft structure are performed to validate the proposed method. The results show that the proposed method can effectively reduce the influence of uncertainties on FCD, which is promising for real engineering applications.

The 1st International Project Competition for Structural Health Monitoring (IPC-SHM, 2020): A summary and benchmark problem

2021 ◽  
pp. 147592172110064
Author(s):  
Yuequan Bao ◽  
Jian Li ◽  
Tomonori Nagayama ◽  
Yang Xu ◽  
Billie F Spencer ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Undergraduate Students ◽  
Health Monitoring ◽  
Fatigue Cracks ◽  
Full Scale ◽  
Asia Pacific ◽  
International Project ◽  
Full Time ◽  
Structural Health ◽  
Institute Of Technology

To promote the development of structural health monitoring around the world, the 1st International Project Competition for Structural Health Monitoring (IPC-SHM, 2020) was initiated and organized in 2020 by the Asia-Pacific Network of Centers for Research in Smart Structures Technology, Harbin Institute of Technology, the University of Illinois at Urbana-Champaign, and four leading companies in the application of structural health monitoring technology. The goal of this competition was to attract more young scholars to engage in the study of structural health monitoring, encouraging them to provide creative and effective solutions for full-scale applications. Recognizing the recent advent and importance of artificial intelligence in structural health monitoring, three competition projects were set up with the data from full-scale bridges: (1) image-based identification of fatigue cracks in bridge girders, (2) data anomaly detection for structural health monitoring, and (3) condition assessment of stay cables using cable tension data. Three corresponding data sets were released at http://www.schm.org.cn and http://sstl.cee.illinois.edu/ipc-shm2020 . Participants were required to be full-time undergraduate students, M.S. students, Ph.D. students, or young scholars within 3 years after obtaining their Ph.D. Both individual and teams (each team had no more than five individuals) could compete. Submissions for the competition included a 10- to 15-page technical paper, a 10-min presentation video with PowerPoint slides, and commented code. The organizing committee then conducted the validation, review, and evaluation. A total of 330 participants in 112 teams from 70 universities and institutions in 12 countries registered for the competition, resulting in 75 papers from 56 teams from 57 different affiliations finally being submitted. Of those submitted, 31, 30, and 14 papers were for Projects 1, 2, and 3, respectively. After completion of the review by the organization committee and awards committee, the top 10, 10, and 5 teams were selected as the prize winners for the three competition projects.

scholarly journals Arrays of Conformable Ultrasonic Lamb Wave Transducers for Structural Health Monitoring with Real-time Electronics

2014 ◽  
Vol 87 ◽  
pp. 1266-1269 ◽  
Author(s):  
L. Capineri ◽  
A. Bulletti ◽  
M. Calzolai ◽  
P. Giannelli ◽  
D. Francesconi
Keyword(s):  
Structural Health Monitoring ◽  
Real Time ◽  
Health Monitoring ◽  
Lamb Wave ◽  
Structural Health

scholarly journals Structural Health Monitoring Method of Pantograph–Catenary System Based on Strain Response Inversion

2021 ◽  
Vol 9 ◽  
Author(s):  
Sheng Liu ◽  
Yibo Wei ◽  
Yongxin Yin ◽  
Tangzheng Feng ◽  
Jinbao Lin
Keyword(s):  
Structural Health Monitoring ◽  
Contact Force ◽  
Health Monitoring ◽  
Data Consistency ◽  
Inversion Method ◽  
Maximum Speed ◽  
Strain Response ◽  
Monitoring Method ◽  
Structural Health ◽  
Catenary System

Pantograph-catenary system provides electric energy for the subway lines; its health status is essential to the serviceability of the vehicle. In this study, a real-time structural health monitoring method based on strain response inversion is proposed to calculate the magnitude and position of the dynamic contact force between the catenary and pantograph. The measurement principle, calibration, and installation detail of the fiber Bragg grating (FBG) sensors are also presented in this article. Putting this monitoring system in use, an application example of a subway with a rigid overhead catenary is given to demonstrate its performance. The pantograph was monitored and analyzed, running underground at a maximum speed of 80 km/h. The results show that the strain response inversion method has high measurement accuracy, good data consistency, and flexibility on sensor installation. It can accurately calculate the magnitude and location of the contact force exerted on the pantograph.

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