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A recursive Bayesian framework for structural health management using online monitoring and periodic inspections

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  • Rabiei, Masoud
  • Modarres, Mohammad

Abstract

The necessary information for developing a structural health diagnostic and prognostic solution is often obtained from various sources. This paper presents a Bayesian framework for online integration of the structural health assessment information obtained from empirical crack growth models, structural health monitoring and periodic inspections. The data used in Bayesian updating could be direct damage observations (e.g., observed crack sizes) and/or damage growth rate estimates (e.g., crack growth rate observations). An AE-based monitoring approach is used to obtain the crack growth rate observations in this paper. The outcome of this approach is updated crack size distribution as well as updated parameters for an empirical crack growth model. The model with updated parameters is used for prognosis given an assumed future usage profile.

Suggested Citation

  • Rabiei, Masoud & Modarres, Mohammad, 2013. "A recursive Bayesian framework for structural health management using online monitoring and periodic inspections," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 154-164.
    • Handle: RePEc:eee:reensy:v:112:y:2013:i:c:p:154-164
    DOI: 10.1016/j.ress.2012.11.020
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    References listed on IDEAS

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    1. Wang, X. & Rabiei, M. & Hurtado, J. & Modarres, M. & Hoffman, P., 2009. "A probabilistic-based airframe integrity management model," Reliability Engineering and System Safety, Elsevier, vol. 94(5), pages 932-941.
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    Cited by:

    1. Tae San Kim & So Young Sohn, 2021. "Multitask learning for health condition identification and remaining useful life prediction: deep convolutional neural network approach," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2169-2179, December.
    2. Samarakoon, Samindi M.K. & Ratnayake, R.M. Chandima, 2015. "Strengthening, modification and repair techniques’ prioritization for structural integrity control of ageing offshore structures," Reliability Engineering and System Safety, Elsevier, vol. 135(C), pages 15-26.
    3. Mengyao Gu & Jiangqin Ge, 2023. "Research on health state assessment and prediction for complex equipment based on the improved FMECA and GM (1,1)," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 523-538, March.
    4. 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.
    5. Jiang, Shan & Li, Yan-Fu, 2021. "Dynamic Reliability Assessment of Multi-cracked Structure under Fatigue Loading via Multi-State Physics Model," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    6. Lyu, Dongzhen & Niu, Guangxing & Liu, Enhui & Zhang, Bin & Chen, Gang & Yang, Tao & Zio, Enrico, 2022. "Time space modelling for fault diagnosis and prognosis with uncertainty management: A general theoretical formulation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    7. Datteo, Alessio & Busca, Giorgio & Quattromani, Gianluca & Cigada, Alfredo, 2018. "On the use of AR models for SHM: A global sensitivity and uncertainty analysis framework," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 99-115.

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