A major problem of structural health monitoring (SHM) has been the prognosis of damage and the definition of the remaining useful life of a structure. Both tasks depend on many parameters, many of which are often uncertain. Many models have been developed for the aforementioned tasks but they have been either deterministic or stochastic with the ability to take into account only a restricted amount of past states of the structure. In the current work, a generative model is proposed in order to make predictions about the damage evolution of structures. The model is able to perform in a population-based SHM (PBSHM) framework, to take into account many past states of the damaged structure, to incorporate uncertainties in the modelling process and to generate potential damage evolution outcomes according to data acquired from a structure. The algorithm is tested on a simulated damage evolution example and the results reveal that it is able to provide quite confident predictions about the remaining useful life of structures within a population.
翻译:结构性健康监测(SHM)的一个主要问题是对损害的预测和结构剩余使用寿命的定义,这两个任务都取决于许多参数,其中许多往往是不确定的。许多模型是为上述任务开发的,但它们具有确定性或随机性,只能够考虑到结构过去有限程度的过去状况。在目前的工作中,提议了一个基因模型,以便对结构的损害演变作出预测。该模型能够在以人口为基础的SHM(PBSHM)框架内运行,考虑到受损结构的许多过去状态,将不确定性纳入建模过程,并根据从一个结构中获得的数据产生潜在的损害演变结果。算法根据模拟损害演变实例进行测试,结果显示,它能够对人口结构的剩余有用寿命提供非常可靠的预测。