关键词: 结构健康管理, 数字孪生, 降阶模型, 疲劳寿命预测, 飞行和维修规划

Abstract: Aiming at the urgent needs of high-precision real-time life prediction and intelligent operation and maintenance in aircraft structural health management, this paper proposes an aircraft structural health management approach based on flight parameter-load-life digital twin models. First, measured data of flight parameters and loads in structural key parts are used to train flight parameter-load digital twin models based on the incremental learning eXtreme Gradient Boosting (XGBoost), so as to realize high-precision dynamic mapping of the loads of the key parts. Secondly, simulated data of parametric models are used to train load-stress field digital twin models based on the non-intrusive reduced-order technique to realize high-precision dynamic reconstruction of the stress field of the key parts. Furthermore, the fatigue life estimation model is constructed based on the Detail Fatigue Rating (DFR) method. The parameters of the fatigue life estimation model are calculated from the prediction results of the above digital twin models, and the life consumption is estimated to realize the high-accuracy dynamic prediction of the remaining life, forming a flight parameter-load-life digital twin model. On this basis, fleet maintenance and flight task multi-objective planning models are constructed respectively, and solution sets of historical planning problems are formed by intelligent optimization algorithm. Then, based on clustering center distance and coefficient of determination metrics, similarity between historical planning (source-domain) problems and new planning (target-domain) problem in terms of solution set distribution and feature space is quantified. Finally, the highly similar source-domain problem solutions are transferred to the initial population of the target-domain problems to form an intelligent planning method for fleet maintenance and flight task based on transfer learning of historical information, which realizes the intelligent and efficient management of fleet life. The results of a typical flight-testing example show that this method can dynamically predict the loads and remaining life of the structural key parts with a load prediction error of 5.30% and a life prediction error of -7.19%. Meanwhile, for the complex maintenance and flight task planning problems of 20 aircraft, the efficiency of the proposed method is improved by 33.9% and 14.5%, respectively, compared with the direct optimization method, which verifies the effectiveness of the proposed method.

Key words: Structural health management, Digital twin, Reduced order model, Fatigue life prediction, Flight and maintenance planning