In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L₁-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L₁-norm, reducing the estimation noise while increasing the accuracy. The L₁-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L₁-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

在本文中，提出了一种基于模型的燃气轮机传感器故障检测和估计的新方法。所提出的方法包括驱动燃气轮机的状态空间模型，设计基于L ₁范数Lyapunov的新型观测器以及基于观测器库的决策逻辑。使用基于L ₁范数的多个Lyapunov函数设计了新颖的观察器，从而在提高准确度的同时减少了估计噪声。的大号₁范数观察者类似于切换时间滑模观测。提出的观察者还充当低通滤波器，从而减少估计抖动。由于在基于模型的传感器故障检测中需要一组观察员，因此一组L本文设计了₁ -norm观察者。对应于观察者库的使用，开发了两步故障检测决策逻辑。此外，所提出的状态空间模型是一种混合数据驱动模型，根据燃气轮机的性质，该模型分为稳态和瞬态两个模型。该模型是通过将子空间算法应用于SGT-600（工业燃气轮机）的实际数据而开发的。通过应用于其他两个具有不同环境和运行条件的类似燃气轮机，对提出的模型进行了验证。所提出的方法实施的结果证明了精确的燃气轮机传感器故障检测和估计。