Novel Fatigue-Damage Sensor for Prediction of Remaining Fatigue Lifetime of Mechanical Components and Structures
Publication: Journal of Structural Engineering
Volume 147, Issue 10
Abstract
In this paper, a novel Internet of Things (IoT) fatigue damage sensor is presented to predict the fatigue damage accumulation level and residual fatigue life of critical mechanical and structural components. The intelligent radio-frequency identification (RFID)-integrated fatigue sensor system has special designed breakable mini beams with multiple parallel oriented geometry and symmetric U-type stress concentrated notches. The fatigue sensing beams with different stress and fatigue lifetime levels are designed to estimate the fatigue damage accumulation and remaining fatigue life of unidirectional and multidirectional structural or mechanical elements, including composite structures. The fatigue damage sensor attached on a real structure goes through the same fatigue life experience of critical structural elements or mechanical components from the beginning of service life or any point in the service life to the end of service life. A fatigue sensor with five parallel symmetric U notched mini beams with different fatigue lifetimes (10% N, 25% N, 50% N, 70% N, 85%–90% N) normalized to the total life (N) of a structure are considered to predict the remaining fatigue lifetime of a real structure using the cumulative damage Miner’s rule. The mini fatigue sensor beams attached to the surface of a structure are designed to fail, depending on their fatigue life, earlier than the total fatigue lifetime of the structure, like a fatigue fuse system. The fatigue damage sensing beams fail gradually while passing through the same fatigue cycles as a structural member. When the service lifetime of a real monitored structure is very close to the total fatigue life, the critical maximum lifetime (80%–90% N) notched mini sensor beam breaks and issues an alarm that the service life of the real structural part is approaching its fatigue strength limit and the end of its service life. The smart fatigue damage sensor is wireless-enabled, which makes it possible to check the health status of the sensor over a sensor network as often as one likes. Since the wireless distributed fatigue sensor network system monitors the fatigue health conditions of structures periodically or on demand, the data collected on the sensor network system can be used not only for condition-based fatigue life prediction but also for sensor-based predictive fatigue maintenance and the development of new fatigue design tools for fatigue-sensitive complex and large engineering structures or mechanical systems.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
References
Acenbach, J. D. 2009. “Structural health monitoring—What is the prescription?” J. Mech. Res. Commun. 36 (2): 137–142. https://doi.org/10.1016/j.mechrescom.2008.08.011.
Alshahbouni, T., and A. Güngör. 2019. “Designing and modeling U-notch fatigue sensor to predict the fatigue life of structural components.” Eng. Sci. Tech., Int. J. 22 (2): 405–415. https://doi.org/10.1016/j.jestch.2018.09.011.
Boller, C., and N. Meyendorf. 2008. “State-of-the-art in structural health monitoring for aeronautics.” In Proc., of the Int. Symp. on NDT (Non Destructive Testing) in Aerospace. Saarbrücken, Germany: Fraunhofer Institute for Nondestructive Testing.
Correia, J. A., P. Raposo, M. Muniz-Calvente, S. Blasón, G. Lesiuk, A. M. P. De Jesus, P. M. G. P. Moreira, R. A. B. Calçada, and A. F. Canteli. 2017. “A generalization of the fatigue Kohout-Věchet model for several fatigue damage parameters.” Eng. Fract. Mech. 185 (Nov): 284–300. https://doi.org/10.1016/j.engfracmech.2017.06.009.
Fay, M. K., J. D. Kearns, G. Pena, and J. P. Bommer. 2007. Fracture detecting structural health sensor. Washington, DC: US Patent and Trademark Office.
Goldfine, N., V. Zilberstein, A. Washabaugh, D. Schlicker, I. Shay, and D. Grundy. 2006. “Fatigue and stress monitoring with magnetic sensor arrays.” In Proc., Annual Society for Experimental Mechanics (SEM) Conf. Bethel, CT: Society for Experimental Mechanics.
Kacmarcik, J., E. Beslagic, and D. Spahic. 2008. “New mathematical model of stress concentration factor in tension of rectangular bar with opposite edge U–notches.” In Proc., 12th Int. Research Expert Conf. “Trends in the development of Machinery and Associated Technology,” TMT 2008, 26–301. Zenica, Bosnia and Herzegovina: Univ. of Zenica.
Kaplan, H., and T. Ozkul. 2016. “A novel smart fatigue damage sensor for structural health monitoring of critical components of structures.” In Proc., 2016 Int. Conf. on Industrial Informatics and Computer Systems (CIICS), 1–5. New York: IEEE.
Kohout, J., and S. Vechet. 2001. “A new function for fatigue curves characterization and its multiple merits.” Int. J. Fatigue 23 (2): 175–183. https://doi.org/10.1016/S0142-1123(00)00082-7.
Lei, Q., S. Yuan, Q. Wang, Y. Sun, and Y. Weiwei. 2009. “Design an experiment of PZT network-based structural health monitoring scanning system.” Chin. J. Aeronaut. 22 (5): 505–512. https://doi.org/10.1016/S1000-9361(08)60133-8.
Mascarenasa, D. L., E. B. Flynna, M. D. Todda, T. G. Overlyb, K. M. Farinholtb, G. Parkb, and R. Farrar. 2010. “Experimental studies of using wireless energy transmission for powering embedded sensor nodes.” J. Sound Vib. 329 (12): 2421–2433. https://doi.org/10.1016/j.jsv.2009.10.023.
Ozkul, T., H. Kaplan, and M. Dolen. 2014. Wireless enabled fatigue sensor for structural health monitoring. Washington, DC: US Patent and Trademark Office.
Papazian, J. M., et al. 2007. “Sensors for monitoring early stage fatigue cracking.” Int. J. Fatigue 29 (9–11): 1668–1680. https://doi.org/10.1016/j.ijfatigue.2007.01.023.
Phares, B. M. 2007. “The electrochemical fatigue sensor: A novel sensor for active fatigue crack detection and characterization material technologies.” In Proc., 3rd Int. Conf. on Structural Health Monitoring of Intelligent Infrastructure, 126. Red Bank, NJ: Material Tecnologies Inc.
Wijesinghe, B. P., S. A. Zacharie, K. D. Mish, and J. D. Baldwin. 2013. “Design and development of in situ fatigue sensors for structural health monitoring of highway bridges.” J. Bridge Eng. 18 (4): 297–307. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000361.
Williamson, C., and L. Fixter. 2006. State of the art review—Structural health monitoring. London: NAMTEC and the Institute of Materials Minerals and Mining.
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Published In
Journal of Structural Engineering
Volume 147 • Issue 10 • October 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 30, 2020
Accepted: Apr 19, 2021
Published online: Aug 4, 2021
Published in print: Oct 1, 2021
Discussion open until: Jan 4, 2022
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