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Dual crack growth prognosis by using a mixture proposal particle filter and on-line crack monitoring

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  • Chen, Jian
  • Yuan, Shenfang
  • Sbarufatti, Claudio
  • Jin, Xin

Abstract

On-line prognosis of fatigue cracks in the structure is challenging due to various uncertainties affecting fatigue crack initiation and growth. This paper proposes an on-line prognosis strategy for fatigue cracks by incorporating the mixture proposal particle filter (MPPF) and structural health monitoring (SHM) results. In this method, a dynamic crack evolution model is proposed to deal with the situation that more than one crack occurs and grows in the structure. Meanwhile, crack sizes monitored by the SHM technique are incorporated to construct an effective mixture proposal of the importance probability density, which is the key for sampling new particles. Further, posterior estimations of the fatigue crack sizes and the crack evolution model parameters are evaluated with these particles, based on which the prognosis of fatigue crack growth is carried out. A leave-one-out validation is performed on the dual crack growth problem of the hole-edge-cracked structure, demonstrating the effectiveness of the proposed method.

Suggested Citation

  • Chen, Jian & Yuan, Shenfang & Sbarufatti, Claudio & Jin, Xin, 2021. "Dual crack growth prognosis by using a mixture proposal particle filter and on-line crack monitoring," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:reensy:v:215:y:2021:i:c:s0951832021002878
    DOI: 10.1016/j.ress.2021.107758
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    Cited by:

    1. Khakifirooz, Marzieh & Fathi, Michel & Lee, I-Chen & Tseng, Sheng-Tsaing, 2023. "Neural ordinary differential equation for sequential optimal design of fatigue test under accelerated life test analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Gu, Hang-Hang & Wang, Run-Zi & Tang, Min-Jin & Zhang, Xian-Cheng & Tu, Shan-Tung, 2024. "Data-physics-model based fatigue reliability assessment methodology for high-temperature components and its application in steam turbine rotor," Reliability Engineering and System Safety, Elsevier, vol. 241(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. Aizpurua, J.I. & Stewart, B.G. & McArthur, S.D.J. & Penalba, M. & Barrenetxea, M. & Muxika, E. & Ringwood, J.V., 2022. "Probabilistic forecasting informed failure prognostics framework for improved RUL prediction under uncertainty: A transformer case study," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. Kim, Wongon & Lee, Guesuk & Son, Hyejeong & Choi, Hyunhee & Youn, Byeng D., 2022. "Estimation of fatigue crack initiation and growth in engineering product development using a digital twin approach," Reliability Engineering and System Safety, Elsevier, vol. 226(C).

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