Fatigue Damage Prognostics and Life Prediction with Dynamic Response Reconstruction Using Indirect Sensor Measurements

Fatigue Damage Prognostics and Life Prediction with Dynamic Response Reconstruction Using Indirect Sensor Measurements

Jingjing He (Clarkson University, USA), Xuefei Guan (Clarkson University, USA) and Yongming Liu (Clarkson University, USA)
DOI: 10.4018/978-1-4666-2095-7.ch019
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This study presents a general methodology for fatigue damage prognostics and life prediction integrating the structural health monitoring system. A new method for structure response reconstruction of critical locations using measurements from remote sensors is developed. The method is based on the empirical mode decomposition with intermittency criteria and transformation equations derived from finite element modeling. Dynamic responses measured from usage monitoring system or sensors at available locations are decomposed into modal responses directly in time domain. Transformation equations based on finite element modeling are used to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. The mode superposition method is employed to obtain dynamic responses at critical locations for fatigue crack propagation analysis. Fatigue analysis and life prediction can be performed given reconstructed responses at the critical location. The method is demonstrated using a multi degree-of-freedom cantilever beam problem.
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The issue of predicting the remaining useful life (RUL) of machines has attracted considerable attention in recent years. Among the various aging effects that influence the remaining useful life, fatigue is one of the most important failure modes. Fatigue prognosis is still a challenging problem for those structures suffered from the aging problem. The main objective of fatigue prognosis is to predict the remaining useful life of engineering materials and structures under cyclic loadings. The prognosis methods can be generally classified to two major approaches: data-driven method and physics-based method. Data-driven methods are applicable where the physics of the problem does not change much. For example, the loading spectrum of training samples needs to be similar with those of predictions. This requirement limits the applicability of the data-driven approaches. This paper uses physics-based models for damage prognosis, which are capable of different random loading spectrums. One of the most uncertain factors of a reliable fatigue RUL prediction is the loading uncertainty. Classical fatigue damage tolerance analysis and design used specified design spectrums for the entire fleet. The progress of structural health monitoring and usage monitoring systems makes it possible for the fatigue damage prognosis to use measured loading spectrums for each individual vehicle, which will significantly advance the next generation vehicle health management(Gupta, Ray, & Keller, 2007; Link & Weiland, 2009; Papazian et al., 2007). Several advanced sensor techniques have been developed for usage monitoring system. The usage monitoring system collects data for the usage information, such as mechanical load, temperature, humidity, etc. of a system (Chang, 1998). The information from usage monitoring system greatly facilitates the accurate RUL prediction of an individual structural system under service loading conditions. One of the objectives of this study is to propose a general methodology for the fatigue damage prognosis integrating usage monitoring system.

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