Abstract
The method of constructing an empirical model is used to compensate the deviation between the output of the on-board real-time model and the engine measurement parameters, and improve the parameter tracking and estimation performance of the on-board adaptive model in the full flight envelope. Due to the large amount of data acquired online, the clustering method based on Gaussian mixture model is implemented to realize data compression for offline training and updating the empirical model. The present empirical model is applied to the on-board adaptive model of civil large bypass ratio turbofan engine. The simulation results show that the empirical model based on Gaussian mixture model can reduce the output error of on-board real-time model, and the accuracy of the health parameter estimation and engine component fault isolation performance of the on-board real-time adaptive model with empirical model are improved.
Highlights
针对每一组在线获取的输入参数和测量参数, 首先依据飞行高度和马赫数确定当前工况所属飞行 包线的子区域,针对包线内每个子区域建立 GMM, 在飞行包线内单个子区域的 GMM 的内部结构如图 2 所示。
The method of constructing an empirical model is used to compensate the deviation between the output of the on⁃board real⁃time model and the engine measurement parameters, and improve the parameter tracking and esti⁃ mation performance of the on⁃board adaptive model in the full flight envelope
Due to the large amount of data ac⁃ quired online, the clustering method based on Gaussian mixture model is implemented to realize data compression for offline training and updating the empirical model
Summary
针对每一组在线获取的输入参数和测量参数, 首先依据飞行高度和马赫数确定当前工况所属飞行 包线的子区域,针对包线内每个子区域建立 GMM, 在飞行包线内单个子区域的 GMM 的内部结构如图 2 所示。 GMM 是由平均值和标准偏差确定的多维高斯 分布,模型输入向量和残差向量中各元素的平均值 和标准偏差的递推计算如( 1) 式所示: 1) 输入参数 ui ,σ(i u) 若当前加载的 GMM 模型不能表征当前获取的 飞行数据,则需要建立新的 GMM 模型,建立流程图 如图 5 所示。 N ≥ Nmax í îïïd( u(1) ,u( N) ) ≥ Threshold2
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