基于LMI和离散模型的航空发动机压气机传感器鲁棒故障诊断

针对航空发动机压气机健康监测提出了一种基于线性矩阵不等式(LMI)和H_∞优化理论的航空发动机压气机传感器鲁棒故障诊断的方法.在航空发动机具有模型不确定性和外界噪声的情况下,应用基于神经网络的线性拟合方法实现航空发动机压气机离散模型的建立;并通过LMI和H_∞优化问题的求解得到未知输入观测器的设计参数,实现具有强鲁棒性的传感器故障诊断.该方法比以前研究中未知输入观测器故障诊断方法的优点在于能够同时处理模型不确定性和外界噪声.应用ALSTOM公司提供的燃气涡轮压气机模型进行了仿真验证,在压气机具有白噪声模型误差和正弦外界干扰的情况下,实现对小于测量范围2%的传感器故障的检测和诊断. 

Robust fault diagnosis for aero-engine compressor sensor based on LMI and discrete model

A robust fault diagnosis approach for aero-engine compressor sensor based on the linear matrix inequality(LMI) and the H_∞ optimization theory was presented for aero-engine compressor health monitoring. The aero-engine compressor discrete model was obtained by using the linear fitting approach based on the neural network in consideration of both the uncertainties of model and noise. The design parameters of unknown input observer (UIO) were obtained by solving LMI and H_∞ optimization for fault diagnosis of sensor with strong robustness. The main advantage of this approach lies in that it can handle both uncertainties of model and noise simultaneously compared with fault diagnosis approach of UIO existing. The numerical simulation was conducted by using the gas turbine compressor model provided by ALSTOM Company. The result shows that less than 2% sensor fault can be detected under white noise modeling uncertainty and sinusoidal external disturbance.