Zhang Yan, Zhang Yongbo, Yu Jinhui, Zhao Fei, Zhu Shihao
School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China.
Aircraft and Propulsion Laboratory, Ningbo Institute of Technology, Beihang University, Ningbo 315100, China.
Sensors (Basel). 2024 Nov 27;24(23):7582. doi: 10.3390/s24237582.
As sensor monitoring technology continues to evolve, structural online monitoring and health management have found numerous applications across various fields. However, challenges remain concerning the real-time diagnosis of structural damage and the accuracy of dynamic reliability predictions. In this paper, a structural online damage identification and dynamic reliability prediction method based on Unscented Kalman Filter (UKF) is presented. Specifically, in the Wiener degradation process with random effects on structural performance, the structural damage identification is initially realized using UKF. Following that, the EM algorithm is employed for estimating the performance model parameters. Eventually, dynamic reliability prediction is realized based on conditional probability. The simulation results indicate that the method effectively estimates the damage state during the structure's use while providing accurate, real-time, and dynamic reliability predictions for the system.
随着传感器监测技术不断发展，结构在线监测与健康管理在各个领域都有了广泛应用。然而，在结构损伤的实时诊断以及动态可靠性预测的准确性方面仍存在挑战。本文提出了一种基于无迹卡尔曼滤波器（UKF）的结构在线损伤识别与动态可靠性预测方法。具体而言，在对结构性能有随机影响的维纳退化过程中，首先利用UKF实现结构损伤识别。随后，采用期望最大化（EM）算法估计性能模型参数。最终，基于条件概率实现动态可靠性预测。仿真结果表明，该方法能有效估计结构使用过程中的损伤状态，同时为系统提供准确、实时的动态可靠性预测。
