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Cited by 13Ultrasonics and Acoustic Wave PropagationStructural Health Monitoring TechniquesNon-Destructive Testing Techniques

Statistical guided-waves-based structural health monitoring via stochastic non-parametric time series models

Open access
Amer, Ahmad, Kopsaftopoulos, Fotis P·2022·Structural Health Monitoring
DOI 10.1177/14759217211024527
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Abstract

Damage detection in active-sensing, guided-waves-based structural health monitoring (SHM) has evolved through multiple eras of development during the past decades. Nevertheless, there still exist a number of challenges facing the current state-of-the-art approaches, both in the industry as well as in research and development, including low damage sensitivity, lack of robustness to uncertainties, need for user-defined thresholds, and non-uniform response across a sensor network. In this work, a novel statistical framework is proposed for active-sensing SHM based on the use of ultrasonic guided waves. This framework is based on stochastic non-parametric time series models and their corresponding statistical properties in order to readily provide healthy confidence bounds and enable accurate and robust damage detection via the use of appropriate statistical decision-making tests. Three such methods and corresponding statistical quantities (test statistics) along with decision-making schemes are formulated and experimentally assessed via the use of three coupons with different levels of complexity: an Al plate with a growing notch, a carbon fiber-reinforced plastic (CFRP) plate with added weights to simulate local damage, and the CFRP panel used in the Open Guided Waves project, all fitted with piezoelectric transducers under a pitch-catch configuration. The performance of the proposed methods is compared to that of state-of-the-art time-domain damage indices (DIs). The results demonstrate the increased detection sensitivity and robustness of the proposed methods, with better tracking capability of damage evolution compared to conventional approaches, even for damage-non-intersecting actuator–sensor paths. In particular, the Z statistic emerges as the best damage detection metric compared to conventional DIs, as well as the other proposed statistics. Overall, the proposed statistics in this study promise greater damage sensitivity across different components, with enhanced robustness to uncertainties, as well as user-friendly application.

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  • Active SHM enables direct and targeted detection and evaluation of structural damage, resulting in more reliable and accurate health assessment.

    “On the other hand, active SHM enables the direct and targeted detection and evaluation of structural damage resulting in more reliable and accurate health assessment.”

    From Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review (2023) · cites this paper as [59]

    The Introduction (p. 3) describes active-sensing guided-wave SHM methods for detecting and quantifying damage. But the Abstract (p. 1) attributes accurate, robust detection to this paper’s proposed statistical framework, while noting limitations in existing approaches. The source does not establish that active SHM generally produces more reliable and accurate health assessments. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 7

  1. [22][22] He, J., Ran, Y., Liu, B., Yang, J., and Guan, X., 'A Lamb wave based fatigue crack length estimation method using finite element simulations,' The 9th International Symposium on NDT in Aerospace, Xiamen, China , November 2017. -
  2. [27][27] Giurgiutiu, V., 'Flutter prediction for flight/wind-tunnel flutter test under atmospheric turbulence excitation,' Journal of Intelligent Materials Systems and Structures , Vol. 16, No. 4, 2005, pp. 291-305. -
  3. [53][53] Kopsaftopoulos, F. P. and Fassois, S. D., 'Experimental assessment of time series methods for structural health monitoring (SHM),' Proceedings of the 4th European Workshop on Structural Health Monitoring (EWSHM) , Cracow, Poland, 2008. -
  4. [54][54] Kopsaftopoulos, F. P. and Fassois, S. D., 'Vibration Based Health Monitoring for a Thin Aluminum Plate - A Comparative Assessment of Statistical Time Series Methods,' Proceedings of the 5th European Workshop on Structural Health Monitoring (EWSHM) , Sorrento, Italy, 2010. -
  5. [55][55] Box, G. E. P., Jenkins, G. M., and Reinsel, G. C., Time Series Analysis: Forecasting & Control , Prentice Hall: Englewood Cliffs, NJ, 3rd ed., 1994. -
  6. [57][57] Kay, S. M., Modern Spectral Estimation: Theory and Application , Prentice Hall: New Jersey, 1988. -
  7. [58][58] Hayes, M. H., Statistical Digital Signal Processing and Modelling , John Wiley and Sons, New York, 1996. -

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Authors
Amer, A., & Kopsaftopoulos, F. P.
Affiliations
Rensselaer Polytechnic Institute (US)
Corresponding author
Kopsaftopoulos, Fotis P
ORCID
Kopsaftopoulos, Fotis P
Venue
Structural Health Monitoring
Publisher
SAGE Publications
Published
2021-06-19
Volume
21
Issue
3
Pages
1139-1166
ISSN
  • 1475-9217 (ISSN-L)
  • 1741-3168
DOI
10.1177/14759217211024527
OpenAlex
W3123924985
Cited by
13
References
50
Citations by year
2026: 5 · 2025: 1 · 2023: 5 · 2022: 2
FWCI
0.86
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Structural Health Monitoring (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
Vertical Lift Research Center of Excellence (W911W61120012)
Topics
  • Physical Sciences › Engineering › Mechanics of Materials › Ultrasonics and Acoustic Wave Propagation
  • Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
  • Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
Keywords
ultrasonic guided waves, active sensing, damage detection, confidence limits, damage index, carbon fiber reinforced polymer, piezoelectric transducers, pitch-catch configuration

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