Robust sensor fault detection and isolation of gas turbine engines subjected to time-varying parameter uncertainties ☆
Robust sensor fault detection and isolation of gas turbine engines subjected to time-varying parameter uncertainties ☆
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DOI：
10.1016/j.ymssp.2016.02.023
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发表时间：
2016-08
影响因子：
8.4
通讯作者：
B. Pourbabaee;N. Meskin;K. Khorasani
中科院分区：
文献类型：
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作者：
B. Pourbabaee;N. Meskin;K. Khorasani
提出了一种新的基于多模型方法的传感器故障检测与隔离策略，该策略对时变的参数不确定性以及所有通道中的过程和测量噪声都保持了鲁棒性。该方案由针对多个分段线性(PWL)模型构造的鲁棒卡尔曼滤波器(RKF)组成，这些模型是在不确定非线性系统的不同工作点构造的。利用影响所有PWL状态空间矩阵的时变范数有界容许结构来模拟参数不确定性。通过求解表示为两个线性矩阵不等式(LMI)可行性条件的两个代数Riccati方程(ARE)来设计鲁棒卡尔曼滤波增益矩阵。针对存在参数不确定性、过程噪声和测量噪声的单轴燃气轮机，对所提出的基于多RKF的FDI方案进行了仿真，以诊断各种传感器故障。我们的比较研究证实了我们提出的FDI方法与文献中提供的方法相比的优越性。
In this paper, a novel robust sensor fault detection and isolation (FDI) strategy using the multiple model-based (MM) approach is proposed that remains robust with respect to both time-varying parameter uncertainties and process and measurement noise in all the channels. The scheme is composed of robust Kalman filters (RKF) that are constructed for multiple piecewise linear (PWL) models that are constructed at various operating points of an uncertain nonlinear system. The parameter uncertainty is modeled by using a time-varying norm bounded admissible structure that affects all the PWL state space matrices. The robust Kalman filter gain matrices are designed by solving two algebraic Riccati equations (AREs) that are expressed as two linear matrix inequality (LMI) feasibility conditions. The proposed multiple RKF-based FDI scheme is simulated for a single spool gas turbine engine to diagnose various sensor faults despite the presence of parameter uncertainties, process and measurement noise. Our comparative studies confirm the superiority of our proposed FDI method when compared to the methods that are available in the literature.