論文ID: 22-00095
A practical structural health monitoring has been proposed for evaluating the structural health of a whole mechanical asset by using digital twin with data collected during the operation of the asset. Digital twin can be utilized to predict the remaining useful life by estimating the variation of the physical quantity that dominates the life, even though any records of failure do no exist. However, a mechanical asset includes huge number of local hot spots where structural health should be evaluated, and accordingly, huge man-hours are required to integrate a monitoring system that evaluates the health at all the hot spots by using digital twin. A hierarchical structural health monitoring has been therefore developed to overcome this drawback. In the first stage of the health monitoring, the overview of the mechanical damage of the components included in a asset is evaluated according to a metric, D factor, that defines the cumulative damage of the components, and the assets having relatively large damage are extracted. The extracted assets are then evaluated in detail in the second stage; that is, structural health is checked at the local hot spots. The monitoring system that employs digital twin and the hierarchical health monitoring was applied to the health management of wind turbines. As the result of evaluating the structural health of the main components of wind turbines, about a hundred wind turbines can be prioritized according to the D factor. In this first stage, a surrogate model based on a machine learning was utilized for evaluating the overview of the damage with low computational cost; the approximation error of the D factor was less than 3 % by using the surrogate model. It can be therefore concluded that this practical structural health monitoring is useful for the decision making of fleet health management of mechanical assets.
