Methodology for Detecting Progressive Damage in Structures Using Ultrasound-Guided Waves

被引:4
作者
Aranguren, Gerardo [1 ]
Bilbao, Javier [2 ]
Etxaniz, Josu [1 ]
Miguel Gil-Garcia, Jose [3 ]
Rebollar, Carolina [2 ]
机构
[1] Univ Basque Country UPV EHU, Fac Engn Bilbao, Dept Elect Technol, Bilbao 48013, Spain
[2] Univ Basque Country UPV EHU, Fac Engn Bilbao, Appl Math Dept, Bilbao 48013, Spain
[3] Univ Basque Country UPV EHU, Fac Engn Vitoria, Dept Elect Technol, Vitoria 01006, Spain
关键词
SHM; piezoelectric transducers; corrosion; pattern matching; pattern recognition;
D O I
10.3390/s22041692
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Damage detection in structural health monitoring of metallic or composite structures depends on several factors, including the sensor technology and the type of defect that is under the spotlight. Commercial devices generally used to obtain these data neither allow for their installation on board nor permit their scalability when several structures or sensors need to be monitored. This paper introduces self-developed equipment designed to create ultrasonic guided waves and a methodology for the detection of progressive damage, such as corrosion damage in aircraft structures, i.e., algorithms for monitoring such damage. To create slowly changing conditions, aluminum- and carbon-reinforced polymer plates were placed together with seawater to speed up the corrosion process. The setup was completed by an array of 10 piezoelectric transducers driven and sensed by a structural health monitoring ultrasonic system, which generated 100 waveforms per test. The hardware was able to pre-process the raw acquisition to minimize the transmitted data. The experiment was conducted over eight weeks. Three different processing stages were followed to extract information on the degree of corrosion: hardware algorithm, pattern matching, and pattern recognition. The proposed methodology allows for the detection of trends in the progressive degradation of structures.
引用
收藏
页数:16
相关论文
共 41 条
[1]  
Accellent, HARDW AC TECHN
[2]   Fuzzy logic for modeling machining process: a review [J].
Adnan, M. R. H. Mohd ;
Sarkheyli, Arezoo ;
Zain, Azlan Mohd ;
Haron, Habibollah .
ARTIFICIAL INTELLIGENCE REVIEW, 2015, 43 (03) :345-379
[3]   Signal Processing Techniques for Vibration-Based Health Monitoring of Smart Structures [J].
Amezquita-Sanchez, Juan Pablo ;
Adeli, Hojjat .
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING, 2016, 23 (01) :1-15
[4]   Detection and classification of structural changes using artificial immune systems and fuzzy clustering [J].
Anaya, Maribel ;
Alexander Tibaduiza, Diego ;
Pozo, Francesc .
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, 2017, 9 (01) :35-52
[5]  
[Anonymous], 1997, Pattern recognition using neural networks: theory and algorithms for engineers and scientists
[6]   Damage Detection and Characterization in Composites Using a Geometric Modification of the RAPID Algorithm [J].
Azuara, Guillermo ;
Barrera, Eduardo ;
Ruiz, Mariano ;
Bekas, Dimitrios .
IEEE SENSORS JOURNAL, 2020, 20 (04) :2084-2093
[7]  
Cambel A., 1993, APPL CHAOS THEORY PA
[8]   Structural Health Monitoring Using Ultrasonic Guided-Waves and the Degree of Health Index [J].
Cantero-Chinchilla, Sergio ;
Aranguren, Gerardo ;
Royo, Jose Manuel ;
Chiachio, Manuel ;
Etxaniz, Josu ;
Calvo-Echenique, Andrea .
SENSORS, 2021, 21 (03) :1-17
[9]   Ultrasonic Guided-Waves Sensors and Integrated Structural Health Monitoring Systems for Impact Detection and Localization: A Review [J].
Capineri, Lorenzo ;
Bulletti, Andrea .
SENSORS, 2021, 21 (09)
[10]  
Castillero Joseba, 2021, European Workshop on Structural Health Monitoring. Special Collection of 2020 Papers. Lecture Notes in Civil Engineering (LNCE 127), P830, DOI 10.1007/978-3-030-64594-6_80