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Using Gaussian weighting-mixture proposal distribution particle filter for fatigue crack growth prediction
Received date: 2016-11-10
Revised date: 2017-09-08
Online published: 2017-09-08
Supported by
Key Program of National Natural Science Foundation of China (51635008); National Natural Science Foundation of China (51575263); National Natural Science Funds for Distinguished Young Scholars of China (51225502); Priority Academic Program Development of Jiangsu Higher Education Institutions of China; Qing Lan Project of Jiangsu Province of China; Funding of Jiangsu Innovation Program for Graduate Education (KYLX16_0333)
Fatigue crack growth prediction is vital for ensuring structural safety and achieving condition-based maintenance of aircraft. Recently, novel methods for on-line fatigue crack growth prediction have drawn a lot of attention, which combine the particle filter algorithm with structural health monitoring. In these methods, a state space model is developed to represent uncertainties during fatigue crack growth, and the actual crack measurements obtained with structural health monitoring are incorporated to update the result derived from a fatigue crack growth model using Bayesian theorem. However, most of those methods employ the prior probability density function as the importance density in the particle filter algorithm, and thus suffer from the problem of severe particle degeneracy. In addition, the Paris law that can only describe the stable crack growth region is commonly used due to its simplicity. To overcome these problems, this paper proposes an on-line fatigue crack growth prediction method based on the Gaussian weighting-mixture proposal distribution particle filter. The NASGRO model which can describe the entire crack growth region is employed to establish the state equation of fatigue crack growth. The active Lamb wave based monitoring method is used for on-line structural crack monitoring. Based on on-line monitoring, the mixture proposal distribution fusing the prior probability density and the measurement probability density is adopted as the importance density function, and the Gaussian weighting process is proposed with the prior estimate of the crack length. Simulation data are utilized for validation, and the result shows the proposed method can significantly optimize the accuracy of fatigue crack growth prediction.
CHEN Jian , YUAN Shenfang , WANG Hui , QIU Lei . Using Gaussian weighting-mixture proposal distribution particle filter for fatigue crack growth prediction[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2017 , 38(11) : 220925 -220925 . DOI: 10.7527/S1000-6893.2017.220925
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