Fatigue Monitoring in Metallic Structures using Vibration Measurements

Abstract

AbstractA novel framework is proposed for estimating damage accumulation due to fatigue in the entire body of a metallic structure using vibration measurements from a limited number of sensors. Fatigue is estimated using Palmgren-Miner damage rule, S-N curves, rainflow cycle counting of the variable amplitude time histories of the stress components, or frequency domain stochastic fatigue methods based on PSD of the stress components. These methods can be applied to any point in the structure and construct the complete fatigue map of the entire structure, provided that the stress response characteristics (time histories or PSDs) at all desirable points are available. These stress response characteristics are predicted from limited number of vibration sensors using a high fidelity finite element model and different prediction methods, including Kalman filter type techniques, kriging approximations and modal expansion methods. The effectiveness of the proposed methods is demonstrated using simulated data from a chain-like spring-mass model and a small-scale model of a vehicle structure. The proposed framework can be used to construct fatigue damage accumulation and lifetime prediction maps consistent with the actual operational conditions provided by a monitoring system. These maps are useful for designing optimal fatigue-based maintenance strategies for metallic structures taking into account all uncertainties in modeling and fatigue predictions.KeywordsSHMOutput-only measurementsStrain and fatigue predictionsKalman filterModal expansion