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首页> 外文期刊>IEEE Transactions on Control Systems Technology >Sensor Fault Detection, Isolation, and Identification Using Multiple-Model-Based Hybrid Kalman Filter for Gas Turbine Engines
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Sensor Fault Detection, Isolation, and Identification Using Multiple-Model-Based Hybrid Kalman Filter for Gas Turbine Engines

机译：使用基于多模型的混合卡尔曼滤波器对燃气轮机进行传感器故障检测，隔离和识别

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摘要

In this paper, a novel sensor fault detection, isolation, and identification (FDII) strategy is proposed using the multiple-model (MM) approach. The scheme is based on multiple hybrid Kalman filters (MHKFs), which represents an integration of a nonlinear mathematical model of the system with a number of piecewise linear (PWL) models. The proposed fault detection and isolation (FDI) scheme is capable of detecting and isolating sensor faults during the entire operational regime of the system by interpolating the PWL models using a Bayesian approach. Moreover, the proposed MHKF-based FDI scheme is extended to identify the magnitude of a sensor fault using a modified generalized likelihood ratio method that relies on the healthy operational mode of the system. To illustrate the capabilities of our proposed FDII methodology, extensive simulation studies are conducted for a nonlinear gas turbine engine. Various single and concurrent sensor fault scenarios are considered to demonstrate the effectiveness of our proposed online hierarchical MHKF-based FDII scheme under different flight modes. Finally, our proposed hybrid Kalman filter (HKF)-based FDI approach is compared with various filtering methods such as the linear, extended, unscented, and cubature Kalman filters corresponding to both interacting and noninteracting MM-based schemes. Our comparative studies confirm the superiority of our proposed HKF method in terms of promptness of the fault detection, lower false alarm rates, as well as robustness with respect to the engine health parameter degradations.
机译：在本文中，提出了一种使用多模型（MM）方法的新型传感器故障检测，隔离和识别（FDII）策略。该方案基于多个混合卡尔曼滤波器（MHKF），代表了系统的非线性数学模型与许多分段线性（PWL）模型的集成。所提出的故障检测与隔离（FDI）方案能够通过使用贝叶斯方法对PWL模型进行插值来检测和隔离系统整个运行状态下的传感器故障。此外，所提出的基于MHKF的FDI方案已扩展为使用依赖于系统正常运行模式的改进的广义似然比方法来识别传感器故障的幅度。为了说明我们提出的FDII方法的功能，针对非线性燃气涡轮发动机进行了广泛的仿真研究。考虑了各种单个和同时发生的传感器故障场景，以证明我们提出的基于MHKF的在线分层FDII方案在不同飞行模式下的有效性。最后，将我们提出的基于混合卡尔曼滤波器（HKF）的FDI方法与各种滤波方法进行了比较，例如线性，扩展，无味和温和的卡尔曼滤波器，它们分别对应于基于交互和非交互基于MM的方案。我们的比较研究证实了我们提出的HKF方法在故障检测的迅速性，较低的误报率以及相对于发动机健康参数下降的鲁棒性方面的优势。

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