Real-time prognosis of random loaded structures via Bayesian filtering: A preliminary discussion

• Real-time prognostics of damaged structures.
• Particle filtering algorithms for random load fatigue conditions.
• Rayleigh probability density function.
• Bayesian filtering for real-time state and parameter estimation.

Particle filters are effective tools for the monitoring of damage propagation phenomena. However, a common hypothesis of particle filters for damage prognosis is the constant-amplitude fatigue loading affecting the damage growth. This work constitutes a preliminary analysis of the performance of particle filtering in case of random load, relaxing the hypothesis of constant-amplitude fatigue. Two case studies referring to stationary random loads are introduced: the first concerning a narrow-band stress history, while the second focusing on a wide-band stress spectrum. A solution for each case study is provided and validated using numerical simulations of fatigue cracks in a metallic plate.