Particle filter for fatigue crack growth prediction using SH0 wave on-line monitoring.

ABSTRACT

Fatigue crack is one of the main failure modes of pressure vessels. Online monitoring and predicting methods of crack growth play an important role in the operation of important pressure vessel. The SH0 wave is non-dispersive, and it is not disturbed by internal media of pressure vessel and very sensitive to cracks, therefore it is suitable for fatigue crack growth monitoring. Moreover, fatigue crack growth in industry is affected by material properties, loads, which usually shows some uncertainty. And the particle filter (PF) is well suited to deal with prediction problems affected by uncertainty. Hence, the prediction method of crack growth based on SH0 wave monitoring and PF is proposed (short for SH0-PF). The basic theory of crack monitoring method using SH0 wave is introduced, and the signal feature extraction using the damage index is studied. The state equation characterizing the fatigue crack growth is established by Paris model, and the observation equation is established based on the normalized correlation moment damage index according to monitoring signal using SH0 wave. The prediction reliability of the fatigue crack growth applying SH0-PF is verified by experiment with the single edge notched specimen. The experimental results indicate that the prediction accuracy of SH0-PF is better than that of the traditional Paris model.