旋翼振动监测信号的演化分析与损伤跟踪方法

基于非线性涡流(nonlinear eddy current,简称NEC)检测技术搭建了实验系统，对Q195碳素钢和304奥氏体不锈钢两种常用核电结构材料的塑性损伤程度进行无损定量评价研究。发现材料的塑性损伤程度与非线性涡流检测信号频谱图中基频幅值、三次谐波幅值存在一定线性关系。不同材料的线性关系存在差异，Q195碳素钢的检测信号随损伤程度增大而下降，304奥氏体不锈钢的检测信号随损伤程度增大而上升。通过开发实验系统、进行塑性变形导入和非线性涡流检测实验，分析检测信号与塑性变形程度的相关性，发现检测信号中基波幅值及三次谐波幅值与检测试件的塑性变形程度具有良好相关性，验证了本研究方法对两种典型核电结构材料塑性变形无损定量评价的有效性与可行性。

Method on Rotor Health Monitoring Signal Evolution Analysis and Damage Tracking

To address helicopter rotor health monitoring issues and get a kind of the damage sensitivity but disturbance insensitive metrics, the evolving properties of damaged rotor behaviors are investigated in the reconstructed phase space, then a new damage tracking method is developed. First, an aeroelastic model of the rotor system is derived using the finite element method, and the simulated measurements are reconstructed in a higher state space according to the embedding theory. A globally nonlinear reference model to predict the rotor state is formulated using the Volterra series. The difference between the model-estimated state and measured results is used as the state prediction error, the average value of which is evaluated in some disjoint regions of the reconstructed phase space and combined into a damage tracking feature vector. Next, the time series of the damage tracking feature vectors are used directly to extract the dimension fact and trending information about the blade damage by solving an eigenvalue problem. In the case of fault to failure time prediction, the double exponential smoothing method is employed to establish damage trending prognosis models. The feasibility and effectiveness of the proposed method are verified using the data from the blade damage model and the rotor aeroelastic model simulations. The results show that this method can provide fault pattern auto-recognition capabilities and is suitable for tracking the hidden damage in situations in which no pre-knowledge about damage dimension or evolution models is available. The method can also reconstruct the dynamic nature of the underlying system in the phase space using the nonlinear property of the single monitoring signal, which provides a new way to study the system degeneration process in different dimensional spaces with a proper time scale.