A model-based fatigue damage estimation framework of large-scale structural systems

Abstract

A model-based fatigue damage estimation framework is proposed for online estimation of fatigue damage, for structural systems by integrating operational vibration measurements in a high-fidelity, large-scale, finite element (FE) model and applying a fatigue damage accumulation methodology. To proceed with fatigue predictions, one has to infer the stress response time histories characteristics based on the monitoring information contained in vibration measurements collected from a limited number of sensors attached to a structure. Predictions, like the existence, the location, the time, and the extent of the damage, are possible if one combines the information in the measurements with information obtained from a high-fidelity FE model of the structure. Such a model may be optimized with respect to the data, using state-of-the-art FE model updating techniques. These methods provide much more comprehensive information about the condition of the monitored system than the analysis of raw data. The diagnosed degradation state, along with its identified uncertainties, can be incorporated into robust reliability tools for updating predictions of the residual useful lifetime of structural components and safety against various failure modes taking into account stochastic models of future loading characteristics. Fatigue is estimated using the Palmgren–Miner damage rule, S-N curves, and rainflow cycle counting of the variable amplitude time histories of the stress components. Incorporating a numerical model of the structure in the response estimation procedure, permits stress estimation at unmeasured spots. The proposed method is applied in a steel frame of a real city bus.

Optimal Modeling of an Elevator Chassis under Crash Scenario Based on Characterization and Validation of the Hyperelastic Material of Its Shock Absorber System

“…Compared to computationally faster gradientfree local optimizers such as Nelder-Mead simplex direct search algorithm available in MATLAB fminsearch function, CMA-ES is suitable in finding the global optimum, avoiding being trapped at local optima, and has demonstrated rapid convergence capabilities, particularly when searching for a global optimum compared to other evolution algorithms. Furthermore, it is a general purpose method which has been applied successfully to linear and non-linear FE updating problems [19,20], involving large and complex models and cases. Last but not least, it is fully parallelizable, compensating for computational time to convergence when compared to other sequential iterative gradient-free techniques such as the MATLAB fminsearch algorithm.…”

Stress Estimation of Offshore Structures

“…Papadimitriou et al [68] discussed a framework for the remaining fatigue lifetime prognosis using stress/strain estimation. In two papers, Giagopoulos et al [69,70] proposed a framework for updating the system model by applying a covariance matrix adaptation evolution strategy to update a high-fidelity finite element model applied to fatigue damage estimation. Utilising the updated finite element model and the known input/load, Giagopoulos et al [69,70] calculated the strain of structure.…”

“…In two papers, Giagopoulos et al [69,70] proposed a framework for updating the system model by applying a covariance matrix adaptation evolution strategy to update a high-fidelity finite element model applied to fatigue damage estimation. Utilising the updated finite element model and the known input/load, Giagopoulos et al [69,70] calculated the strain of structure. Palanisamy et al [71] applied the Kalman filter and a buffering technique with sensor fusion to estimate the strain response of bottom-fixed offshore structures with quasi-static and non-stationary excitation in a numerical and laboratory study with a circulating water channel setup.…”

“…For stress/strain estimation, these properties are often modal parameters from modal analysis: either experimental modal analysis [77] or operational modal analysis [78]. See [69,70,79,80] for more on model updating using modal parameters as regards finite element models.…”

“…D. Karczub [7] Seo et al [15] Karczub and Norton [16] N. Sehlstedt [17] Hjelm et al [18] Hjelm et al [20] Graugaard-Jensen et al [19] G.M. Lee [21] Pelayo et al [22] Papadimitriou et al [23] Pingle et al [24][25][26][27] Avitabile et al [28] Aenlle et al [29] Papadimitriou et al [30] Avitabile et al [31] Ren and Zhou, [32] Erazo et al [33] Jo and Spencer [34] Palanisamy et al [35] Maes et al [36] Gevinski et al [38] Pelayo et al [37] Baqersad et al [40] Baqersad et al [39] J. Kullaa [41] Iliopoulos et al [44] Maes et al [42] J.Kullaa [43] Dertimanis et al [45] Dertimanis et al [46] Aenlle et al [51] Ren and Zhou [50] Iliopoulos et al [48] Skafte et al [47] Palanisamy et al [49] Giagopoulos et al [52] J. Kullaa [53] Noppe et al [55] Lagerblad et al [58] Lu et al [54] Luthe et al [56] Chen et al [57] Roberts et al [59] Baqersad and Bharadwaj [60] Pedersen et al [66] J. Kullaa [62] Deng et al [61] Nabiyan et al [63] Henkel et al [65] Risaliti et al [64] Bharadwaj et al [67] Giagopoulos et al [70] Okubo et al [13] Seo et al [15] N. Sehlstedt [17] Hjelm et al [18] Hjelm et al [20] Graugaard-Jensen et al [19] G.M. Lee [21] Pelayo et al [22] Papadimitriou et al [23] Pingle et al [24, 25] Pingle et al [26, 27] Avitabile et al [28] Palanisamy et al [71] D. Karczub [7] Aenlle et al [29] Papadimitriou et al [30] Ren and Zhou, [32] Erazo et al [33] Palanisamy et al [35] Maes et al [36] Gevinski et al [38] Pelayo et al [37] Baqersad et al [40] Baqersad et al [39] Iliopoulos et al [44] Maes et al [42] J.Kullaa [43] Dertimanis et al [45] Dertimanis et al [46] Aenlle et al [51] Ren and Zhou [50] Iliopoulos et al…”