IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v252y2024ics0951832024005374.html
   My bibliography  Save this article

Structural damage detection and localization via an unsupervised anomaly detection method

Author

Listed:
  • Liu, Jie
  • Li, Qilin
  • Li, Ling
  • An, Senjian

Abstract

This study introduces an unsupervised machine learning framework for damage detection and localization in Structural Health Monitoring (SHM), leveraging dynamic graph convolutional neural networks and Transformer networks. This approach is specifically tailored to overcome the challenge of limited labeled data in SHM, enabling precise analysis and feature synthesis from sensor-derived time series data for accurate damage identification. Incorporating a novel ‘localization score’ enhances the framework’s precision in pinpointing structural damages by integrating data-driven insights with a physics-informed understanding of structural dynamics. Extensive validations on diverse structures, including a benchmark steel structure and a real-world cable-stayed bridge, underscore the framework’s effectiveness in anomaly detection and damage localization, showcasing its potential to safeguard critical infrastructure through advanced data-effective machine learning techniques.

Suggested Citation

  • Liu, Jie & Li, Qilin & Li, Ling & An, Senjian, 2024. "Structural damage detection and localization via an unsupervised anomaly detection method," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:reensy:v:252:y:2024:i:c:s0951832024005374
    DOI: 10.1016/j.ress.2024.110465
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024005374
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110465?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shi, Jihao & Zhang, Xinqi & Zhang, Haoran & Wang, Qiliang & Yan, Jinyue & Xiao, Linda, 2024. "Automated detection and diagnosis of leak fault considering volatility by graph deep probability learning," Applied Energy, Elsevier, vol. 361(C).
    2. Wu, Wen & Cantero-Chinchilla, Sergio & Prescott, Darren & Remenyte-Prescott, Rasa & Chiachío, Manuel, 2024. "A general approach to assessing SHM reliability considering sensor failures based on information theory," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    3. Giannakeas, Ilias N. & Mazaheri, Fatemeh & Bacarreza, Omar & Khodaei, Zahra Sharif & Aliabadi, Ferri M.H., 2023. "Probabilistic residual strength assessment of smart composite aircraft panels using guided waves," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. Jiang, Shengyu & He, Rui & Chen, Guoming & Zhu, Yuan & Shi, Jiaming & Liu, Kang & Chang, Yuanjiang, 2023. "Semi-supervised health assessment of pipeline systems based on optical fiber monitoring," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. Iamsumang, Chonlagarn & Mosleh, Ali & Modarres, Mohammad, 2018. "Monitoring and learning algorithms for dynamic hybrid Bayesian network in on-line system health management applications," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 118-129.
    6. Miele, S. & Karve, P. & Mahadevan, S., 2023. "Multi-fidelity physics-informed machine learning for probabilistic damage diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    7. Coraça, Eduardo M. & Ferreira, Janito V. & Nóbrega, Eurípedes G.O., 2023. "An unsupervised structural health monitoring framework based on Variational Autoencoders and Hidden Markov Models," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    8. Wang, Mengmeng & Incecik, Atilla & Feng, Shizhe & Gupta, M.K. & Królczyk, Grzegorz & Li, Z, 2023. "Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu, Sunquan & Luo, Kai & Fan, Chengguang & Fu, Kangjia & Wu, Xuesong & Chen, Yong & Zhang, Xiang, 2025. "Advancing spacecraft safety and longevity: A review of guided waves-based structural health monitoring," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    2. Lu, Ning & Li, Yan-Feng & Mi, Jinhua & Huang, Hong-Zhong, 2024. "AMFGP: An active learning reliability analysis method based on multi-fidelity Gaussian process surrogate model," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    3. Mei, Fabin & Chen, Hao & Yang, Wenying & Zhai, Guofu, 2024. "A hybrid physics-informed machine learning approach for time-dependent reliability assessment of electromagnetic relays," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    4. Yang, Chen & Xia, Yuanqing, 2024. "Interval Pareto front-based multi-objective robust optimization for sensor placement in structural modal identification," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    5. Kohtz, Sara & Zhao, Junhan & Renteria, Anabel & Lalwani, Anand & Xu, Yanwen & Zhang, Xiaolong & Haran, Kiruba Sivasubramaniam & Senesky, Debbie & Wang, Pingfeng, 2024. "Optimal sensor placement for permanent magnet synchronous motor condition monitoring using a digital twin-assisted fault diagnosis approach," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    6. Wu, Wen & Prescott, Darren & Remenyte-Prescott, Rasa & Saleh, Ali & Ruano, Manuel Chiachio, 2024. "An asset management modelling framework for wind turbine blades considering monitoring system reliability," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    7. Lian, Zheng & Zhou, Zhi-Jie & Hu, Chang-Hua & Wang, Jie & Zhang, Chun-Chao & Zhang, Chao-Li, 2024. "A health assessment method with attribute importance modeling for complex systems using belief rule base," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    8. Qin, Xia & Kaewunruen, Sakdirat, 2024. "Machine learning and traditional approaches in shear reliability of steel fiber reinforced concrete beams," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    9. BahooToroody, Ahmad & De Carlo, Filippo & Paltrinieri, Nicola & Tucci, Mario & Van Gelder, P.H.A.J.M., 2020. "Bayesian regression based condition monitoring approach for effective reliability prediction of random processes in autonomous energy supply operation," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    10. Moradi, Ramin & Cofre-Martel, Sergio & Lopez Droguett, Enrique & Modarres, Mohammad & Groth, Katrina M., 2022. "Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    11. Oster, Matthew R. & King, Ethan & Bakker, Craig & Bhattacharya, Arnab & Chatterjee, Samrat & Pan, Feng, 2023. "Multi-level optimization with the koopman operator for data-driven, domain-aware, and dynamic system security," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    12. Guo, Kai & Ye, Zhisheng & Liu, Datong & Peng, Xiyuan, 2021. "UAV flight control sensing enhancement with a data-driven adaptive fusion model," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    13. Wang, Mengmeng & Incecik, Atilla & Feng, Shizhe & Gupta, M.K. & Królczyk, Grzegorz & Li, Z, 2023. "Damage identification of offshore jacket platforms in a digital twin framework considering optimal sensor placement," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    14. Dourado, Arinan & Viana, Felipe A.C., 2021. "Early life failures and services of industrial asset fleets," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    15. Gámiz, M.L. & Navas-Gómez, F. & Raya-Miranda, R. & Segovia-García, M.C., 2023. "Dynamic reliability and sensitivity analysis based on HMM models with Markovian signal process," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    16. Yang, Chen & Lu, Wanze & Xia, Yuanqing, 2023. "Reliability-constrained optimal attitude-vibration control for rigid-flexible coupling satellite using interval dimension-wise analysis," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    17. Liang, Tao & Wang, Fuli & Wang, Shu & Li, Kang & Mo, Xuelei & Lu, Di, 2024. "Machinery health prognostic with uncertainty for mineral processing using TSC-TimeGAN," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    18. Zheng, Jianqin & Wang, Chang & Liang, Yongtu & Liao, Qi & Li, Zhuochao & Wang, Bohong, 2022. "Deeppipe: A deep-learning method for anomaly detection of multi-product pipelines," Energy, Elsevier, vol. 259(C).
    19. Miao, Xingyuan & Zhao, Hong, 2023. "Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    20. Phan, Hieu Chi & Dhar, Ashutosh Sutra & Bui, Nang Duc, 2023. "Reliability assessment of pipelines crossing strike-slip faults considering modeling uncertainties using ANN models," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:252:y:2024:i:c:s0951832024005374. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.