Department of Mechanical Engineering, Inha University, Incheon 22212, Republic of Korea.
Sensors (Basel). 2023 Feb 22;23(5):2437. doi: 10.3390/s23052437.
In this study, we present an alternative solution for detecting crack damages in rotating shafts under torque fluctuation by directly estimating the reduction in torsional shaft stiffness using the adaptive extended Kalman filter (AEKF) algorithm. A dynamic system model of a rotating shaft for designing AEKF was derived and implemented. An AEKF with a forgetting factor () update was then designed to effectively estimate the time-varying parameter (torsional shaft stiffness) owing to cracks. Both simulation and experimental results demonstrated that the proposed estimation method could not only estimate the decrease in stiffness caused by a crack, but also quantitatively evaluate the fatigue crack growth by directly estimating the shaft torsional stiffness. Another advantage of the proposed approach is that it uses only two cost-effective rotational speed sensors and can be readily implemented in structural health monitoring systems of rotating machinery.
在本研究中，我们提出了一种通过自适应扩展卡尔曼滤波器（AEKF）算法直接估计扭转轴刚度降低的方法，来检测扭矩波动下旋转轴中的裂纹损伤的替代方案。为设计 AEKF，我们推导出了一个旋转轴的动态系统模型，并实现了它。然后，我们设计了一个带有遗忘因子（）更新的 AEKF，以有效地估计由于裂纹引起的时变参数（扭转轴刚度）。仿真和实验结果都表明，所提出的估计方法不仅可以估计由裂纹引起的刚度降低，还可以通过直接估计轴扭转刚度来定量评估疲劳裂纹的扩展。该方法的另一个优点是它只使用两个具有成本效益的转速传感器，并且可以很容易地在旋转机械的结构健康监测系统中实现。
