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Fault detection and diagnosis algorithms for transient state of an open-cycle liquid rocket engine using nonlinear Kalman filter methods

机译:基于非线性卡尔曼滤波方法的开环液体火箭发动机瞬态故障检测与诊断算法

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This paper deals with an application of the fault detection and diagnosis algorithm based on nonlinear Kalman filter methods for transient state of an open-cycle liquid propellant rocket engine. In order to develop the algorithm, we designed two types of the nonlinear Kalman filter which are the extended Kalman filter and unscented Kalman filter with non-linear model of the liquid propellant rocket engine. Then using the measurement data of some important parameters of the engine, the residuals from the nonlinear Kalman filters are obtained. Using the residuals, we can detect and diagnose faults in the components of the engine by using the multiple model method. To confirm the fault detection and diagnosis algorithm, we developed mathematical model of an open-cycle liquid propellant rocket engine and artificially injected various faults such as decreasing turbopump efficiency. And then, perform the fault detection and diagnosis algorithm and check the performance of the algorithm. This process is numerically demonstrated for the open-cycle liquid propellant rocket engine under start-up process by using the simulated measurement data from the mathematical model of the engine.
机译:本文研究了基于非线性卡尔曼滤波方法的故障检测与诊断算法在开环液体推进火箭发动机瞬态过程中的应用。为了开发该算法,我们设计了两种非线性卡尔曼滤波器,分别是扩展卡尔曼滤波器和无味卡尔曼滤波器,并具有液体推进剂火箭发动机的非线性模型。然后使用发动机某些重要参数的测量数据,获得非线性卡尔曼滤波器的残差。使用残差,我们可以使用多模型方法检测和诊断发动机组件中的故障。为了确定故障检测和诊断算法,我们开发了开环液体推进剂火箭发动机的数学模型,并人工注入了各种故障,例如降低了涡轮泵效率。然后,执行故障检测和诊断算法并检查算法的性能。通过使用来自发动机数学模型的模拟测量数据,对开环液态推进剂火箭发动机在启动过程中的过程进行了数值演示。

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