A Multi-Stage Nonlinear Method for Aeroengine Health Parameter Estimation Based on Adjacent Operating Points

Kai Liu, Quanyong Xu, Jihong Zhu
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Abstract

Health parameter estimation is the core of engine Gas Path Analysis (GPA), which is widely adopted for engine safety improvement, as well as for operation and maintenance cost reduction. The major challenge of GPA lies in the contradiction between the high dimensions of parameters under estimation, e.g., health parameters, and the limited measurements obtainable from a small number of sensors. Existent GPA methods for health parameters commonly apply dimension reduction before estimation, leading to information loss and hence inaccurate estimation. To tackle the challenge of limited sensor measurements and to have more system outputs than parameters under estimation, we proposed to augment the output vector of the system model by combining the measurements from multiple adjacent operating points. But the engine model can face the problem of homogenization if using data from adjacent operating points. This can in turn leads to a low identifiability of parameters. We analyze the internal mechanism of such large deviation of the parameter estimation results based on linear models and argue for the need of nonlinear method. Hence, we propose a multi-stage nonlinear parameter estimation method for health parameters, combining biased and unbiased estimation. In our extensive simulations based on 10 output measurements of a JT9D engine, our method can estimate 130% more parameters than the widely-used GPA method, while reducing the maximum estimation error of health parameters from 2.2% to 0.1%.
基于相邻工作点的多阶段航空发动机健康参数估计非线性方法
健康参数估计是发动机气路分析(GPA)的核心,被广泛应用于提高发动机安全性以及降低运行和维护成本。GPA 的主要挑战在于被估算参数(如健康参数)的高维度与从少量传感器获得的有限测量值之间的矛盾。现有的健康参数 GPA 方法通常在估算前进行降维处理,这会导致信息丢失,从而造成估算不准确。为了解决传感器测量值有限的难题,并获得比估算参数更多的系统输出,我们建议通过合并多个相邻工作点的测量值来增强系统模型的输出向量。但是,如果使用相邻工作点的数据,发动机模型可能会面临同质化问题。这反过来又会导致参数的可识别性降低。我们分析了基于线性模型的参数估计结果出现如此大偏差的内在机理,并论证了非线性方法的必要性。因此,我们提出了一种结合有偏和无偏估计的多阶段非线性健康参数估计方法。在基于 JT9D 发动机 10 次输出测量的大量模拟中,我们的方法比广泛使用的 GPA 方法多估算出 130% 的参数,同时将健康参数的最大估算误差从 2.2% 降低到 0.1%。
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