State Estimation for Prediction of Fatigue Life for a Rollercoaster Connection Subjected to Operational Multiaxial Nonproportional Loading

Abstract

Recent research on structural health monitoring has shown the applicability of Kalman filtering for fusion of models and data of dynamic systems. In this study, the augmented Kalman filter (AKF) is combined with a substructuring approach for the estimation of strain time histories at unmeasured locations of a rollercoaster connection, with the purpose of predicting remaining fatigue life. The AKF allows for response estimation in the absence of direct measurements of operational loads and in the presence of noisy measurements at limited locations of the instrumented system. The proposed research implements for the first time the AKF and a substructure with exclusively in-service strain measurements from a full-scale structure. Given that uniaxial fatigue life assessment methods often prove insufficient for large in-service structures with complex geometry and subjected to multiaxial nonproportional loadings, the critical plane method is here employed for estimation of fatigue life. The applicability of a substructuring approach combined with strain estimates from the AKF is demonstrated and evaluated with operational strain measurements obtained from an in-service rollercoaster support structure. It is shown that the proposed methodology and fine-tuning of the AKF parameters result in a close match of predicted and measured strains.