增强稀疏分解及其在叶片振动参数识别中的应用

Enhancing Sparse Decomposition Based Blade Vibration Parameter Identification

叶端定时是发动机叶片监测有效手段,相比于传统的应变片测量方式,叶端定时不仅可以同时监测所有叶片的振动状态,而且不会对叶片本身的振动造成影响。但是叶端定时采样数据存在高度欠采样的特点,针对该问题,提出基于增强稀疏分解(Enhancing sparse decomposition, ESD)的叶片振动参数辨识技术。稀疏分解是一种在冗余字典中对信号进行分解,通过求解优化问题得到信号在冗余字典下最稀疏解的信号处理方法。增强稀疏分解相比于传统的基追踪算法,可以得到更为稀疏的解。在建立了增强稀疏优化模型后采用原对偶内点法对优化问题进行求解,从而实现信号特征参数的有效辨识。将提出的基于增强稀疏分解的叶片振动参数辨识技术应用于不同类型的仿真数据和转子叶片试验台参数辨识,并与传统的MUSIC算法和最小二乘拟合相对比,提出的算法可以有效避免频谱混叠和泄露现象,并滤除其他频率成分的干扰,得到更清晰的谱图。

Blade tip timing is an engine blade monitoring method proposed in recent years, compared with the traditional strain gauge measurement method, it can not only simultaneously monitor the vibration state of all blades, but also will not affect the vibration condition of blades. However, due to the high undersampling characteristics of blade tip timing data, a blade parameter identification technology is proposed based on enhancing sparse decomposition. Sparse decomposition is a signal processing method that decomposes the signal into a sparse solution via a redundant dictionary. Enhancing sparse decomposition improves the traditional basic pursuit whose solution is not sparse enough, and the enhancing sparse decomposition method can effectively reduce the sampling rate. After the enhancing sparse optimization model is established, the original dual intra-point method is used to solve the optimization problem. The proposed blade parameter identification technology based on enhancing sparse decomposition is applied to different types of simulation data and rotor blade test rig to identify blades’ parameter, the effectiveness of the algorithm has been verified.