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Sequential state estimation of nonlinearon-Gaussian systems with stochastic input for turbine degradation estimation

机译:具有随机输入的非线性/非高斯系统的顺序状态估计,用于涡轮机退化估计

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摘要

Health state estimation of inaccessible components in complex systems necessitates effective state estimation techniques using the observable variables of the system. The task becomes much complicated when the system is nonlinearon-Gaussian and it receives stochastic input. In this work, a novel sequential state estimation framework is developed based on particle filtering (PF) scheme for state estimation of general class of nonlinear dynamical systems with stochastic input. Performance of the developed framework is then validated with simulation on a Bivariate Non-stationary Growth Model (BNGM) as a benchmark. In the next step, three-year operating data of an industrial gas turbine engine (GTE) are utilized to verify the effectiveness of the developed framework. A comprehensive thermodynamic model for the GTE is therefore developed to formulate the relation of the observable parameters and the dominant degradation symptoms of the turbine, namely, loss of isentropic efficiency and increase of the mass flow. The results confirm the effectiveness of the developed framework for simultaneous estimation of multiple degradation symptoms in complex systems with noisy measured inputs.
机译:复杂系统中不可访问组件的健康状态估计需要使用系统的可观察变量进行有效的状态估计技术。当系统是非线性/非高斯系统并且接收随机输入时,任务变得非常复杂。在这项工作中,基于粒子滤波(PF)方案开发了一种新颖的顺序状态估计框架,用于对具有随机输入的非线性动力学系统的一般类别进行状态估计。然后,以双变量非平稳增长模型(BNGM)为基准,通过仿真验证开发框架的性能。下一步,将使用工业燃气涡轮发动机(GTE)的三年运行数据来验证所开发框架的有效性。因此,开发了用于GTE的综合热力学模型,以建立可观察参数与涡轮机主要退化症状之间的关系,即等熵效率损失和质量流量增加。结果证实了开发的框架的有效性,该框架可同时估计带有噪声的测量输入的复杂系统中的多种降解症状。

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