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Structural health monitoring of a linear robot by fiber Bragg grating sensors and cyber-physical system

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Abstract

Robots with linear motion provide high accuracy and repeatability for industrial automation but may perform lower precision and stability when working long hours because of mechanical vibrations and thermal deformation caused by internal motors and ball screws. Structural health monitoring (SHM) can identify the discrepancy and avoid unexpected downtime costs, but electromagnetic interference (EMI) from the motor lowers the signal-to-noise ratio (SNR) of conventional wired sensors and may invalidate the feedback controller. This study presents an SHM system based on optical fiber Bragg grating (FBG) sensors, which provide accurate time-deformation relations and frequency spectrum results. The SHM-FBG system contributes to preventative maintenance and compares the dynamic signal of six selected points on a linear robot. The experiments also consider the boundary condition of (a) with spring loading and (b) without spring loading. Short message services (SMS) based on the long-term evolution (LTE) cellular network have converted the linear robot into an internet of things (IoT) device. The SHM-FBG system is designed to provide long-term observation of the electro-mechanical system and will send a short message to the administrator if mechanical vibrations or thermal deformations exceed predefined limits and detect acoustic emission of components before systematic failure.

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References

  1. Almaiah MA, Hajjej F, Ali A, Pasha MF, Almomani O (2022) A novel hybrid trustworthy decentralized authentication and data preservation model for digital healthcare IoT based CPS. Sensors 22(4):1448

    Article  Google Scholar 

  2. Liu YK, Ong SK, Nee AYC (2022) State-of-the-art survey on digital twin implementations. Adv Manuf 10(1):1–23

    Article  Google Scholar 

  3. Scuro C, Lamonaca F, Porzio S, Milani G, Olivito RS (2021) Internet of things (IoT) for masonry structural health monitoring (SHM): overview and examples of innovative systems. Constr Build Mater 290(123092):123092

    Article  Google Scholar 

  4. Ochella S, Shafiee M, Dinmohammadi F (2022) Artificial intelligence in prognostics and health management of engineering systems. Eng Appl Artif Intell 108(104552):104552

    Article  Google Scholar 

  5. Mata J, Santos J, Barateiro J (2022) Using emergent technologies on the structural health monitoring and control of critical infrastructures. Structural Integrity. Springer International Publishing, Cham, pp 541–567

    Google Scholar 

  6. Abhijith VS, Sowmiya B, Sudersan S, Thangavel M, Varalakshmi P (2022) A review on security issues in healthcare cyber-physical systems. Cyber Intelligence and Information Retrieval. Springer Singapore, Singapore, pp 37–48 (Lecture Notes in Networks and Systems)

    Chapter  Google Scholar 

  7. Doghri W, Saddoud A, Fourati LC (2022) Cyber-physical systems for structural health monitoring: sensing technologies and intelligent computing. J Supercomput 78(1):766–809

    Article  Google Scholar 

  8. Ranasinghe K, Sabatini R, Gardi A, Bijjahalli S, Kapoor R, Fahey T, Thangavel K (2022) Advances in integrated system health management for mission-essential and safety-critical aerospace applications. Prog Aerosp Sci 128(100758):100758

    Article  Google Scholar 

  9. Panwar NG, Singh S, Garg A, Gupta AK, Gao L (2021) Recent advancements in battery management system for Li-ion batteries of electric vehicles: future role of digital twin, cyber-physical systems, battery swapping technology, and nondestructive testing. Energ Technol 9(8):2000984

    Article  Google Scholar 

  10. Lee J, Azamfar M, Singh J, Siahpour S (2020) Integration of digital twin and deep learning in cyber-physical systems: towards smart manufacturing. IET Collab Intell Manuf 2(1):34–36

    Article  Google Scholar 

  11. Ante L (2021) Digital twin technology for smart manufacturing and Industry 4.0: a bibliometric analysis of the intellectual structure of the research discourse. Manuf Lett 27:96–102

    Article  Google Scholar 

  12. Khalil A, Al Janaideh M, Aljanaideh K, Kundur D (2022) Transmissibility-based health monitoring of the future connected autonomous vehicles networks. IEEE Trans Veh Technol 71:3633–3647

    Article  Google Scholar 

  13. Zonzini F, Aguzzi C, Gigli L, Sciullo L, Testoni N, De Marchi L, Di Felice M, Cinotti TS, Mennuti C, Marzani A (2020) Structural health monitoring and prognostic of industrial plants and civil structures: a sensor to cloud architecture. IEEE Instrum Meas Mag 23(9):21–27

    Article  Google Scholar 

  14. Li C, Sun L, Xu Z, Wu X, Liang T, Shi W (2020) Experimental investigation and error analysis of high precision FBG displacement sensor for structural health monitoring. Int J Struct Stab Dyn 20(06):2040011

    Article  Google Scholar 

  15. Wang YW, Ni YQ, Wang X (2020) Real-time defect detection of high-speed train wheels by using Bayesian forecasting and dynamic model. Mech Syst Signal Process 139(106654):106654

    Article  Google Scholar 

  16. Presti DL, Massaroni C, Leitão CS, Domingues MD, Sypabekova M, Barrera D, Floris I et al (2020) Fiber Bragg gratings for medical applications and future challenges: a review. IEEE Access Pract Innov Open Solut 8:156863–156888

    Google Scholar 

  17. Massaroni C, Zaltieri M, Presti DL, Nicolo A, Tosi D, Schena E (2021) Fiber Bragg grating sensors for cardiorespiratory monitoring: a review. IEEE Sens J 21(13):14069–14080

    Article  Google Scholar 

  18. Vorathin E, Hafizi ZM, Ismail N, Loman M (2020) Review of high sensitivity fibre-optic pressure sensors for low pressure sensing. Opt Laser Technol 121(105841):105841

    Article  Google Scholar 

  19. Xiong L, Guo Y, Jiang G, Zhou X, Jiang L, Liu H (2021) Six-dimensional force/torque sensor based on fiber Bragg gratings with low coupling. IEEE Trans Ind Electron 68(5):4079–4089

    Article  Google Scholar 

  20. Li T, Guo J, Tan Y, Zhou Z (2020) Recent advances and tendency in fiber Bragg grating-based vibration sensor: a review. IEEE Sens J 20(20):12074–12087

    Article  Google Scholar 

  21. Gao X, Ning T, Zhang C, Xu J, Zheng J, Lin H, Li J, Pei L, You H (2020) A dual-parameter fiber sensor based on few-mode fiber and fiber Bragg grating for strain and temperature sensing. Optics Commun 454(124441):124441

    Article  Google Scholar 

  22. Zhao J, Xu J, Wang C, Liu Y, Yang Z (2020) Experimental demonstration of multi-parameter sensing based on polarized interference of polarization-maintaining few-mode fibers. Opt Express 28(14):20372–20378

    Article  Google Scholar 

  23. Puttnam BJ, Rademacher G, Luís RS (2021) Space-division multiplexing for optical fiber communications. Optica 8(9):1186

    Article  Google Scholar 

  24. Su Y, He Y, Chen H, Li X, Li G (2021) Perspective on mode-division multiplexing. Appl Phys Lett 118(20):200502

    Article  Google Scholar 

  25. Farooq B, Bao J, Li J, Liu T, Yin S (2020) Data-driven predictive maintenance approach for spinning cyber-physical production system. J Shanghai Jiaotong Univ 25(4):453–462

    Article  Google Scholar 

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Funding

The authors gratefully acknowledge financial support for this research from the Ministry of Science and Technology (Republic of China) under Grant MOST 109–2221-E-002-MY3.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Taiwan, University, Taipei, Taiwan

    Hsiang-Wei Ho, Wei-Hsiang Liao, Ching-Yuan Chang & Chien-Ching Ma

  2. Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan

    Hsiang-Wei Ho, Wei-Hsiang Liao, Ching-Yuan Chang & Chien-Ching Ma

Authors
  1. Hsiang-Wei Ho
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  2. Wei-Hsiang Liao
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  3. Ching-Yuan Chang
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  4. Chien-Ching Ma
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Contributions

Hsiang-Wei Ho and Wei-Hsiang Liao contribute to the mathematical model and numerical analysis. Ching-Yuan Chang contributes to the experimental setup and data collection. Chien-Ching Ma contributes to the main structure of this research.

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Correspondence to Chien-Ching Ma.

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The grant’s policy encourages researchers to broaden industrial automation applications and to publish the latest research in a reputable journal. It is a novel contribution to the scientific literature that has not been published elsewhere previously or simultaneously in whole or in part.

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Ho, HW., Liao, WH., Chang, CY. et al. Structural health monitoring of a linear robot by fiber Bragg grating sensors and cyber-physical system. Int J Adv Manuf Technol 122, 3983–3995 (2022). https://doi.org/10.1007/s00170-022-10066-w

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  • DOI: https://doi.org/10.1007/s00170-022-10066-w

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