Kinetic-motion dissimilar monitoring based inertial data fault detection for flight control system safety enhancement
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摘要:
针对飞控系统多余度惯性数据存在的共模风险抑制、奇异故障正确表决及单余度数据可用性三大工程难题，提出一种引入运动非相似监控的故障检测算法及余度管理架构。其核心是通过简化模型的工程化过载软重构计算建立姿态、角速率、过载及体轴线加速度关系，实现对惯性数据的故障检测，达到飞控系统安全增强的目的。使用实际试飞数据构建地面离线试飞验证平台，通过飞机在严苛边界条件下的试飞数据对所提算法进行校验。结果表明：在惯性数据正常情况下通过飞机动力学关系理论计算的体轴过载与飞机实际测量过载高度一致，最大偏差量小于0.1
g ；当惯性数据中角速率、过载或姿态任一信号故障后，理论计算的体轴过载与实际测量的过载存在明显差异，可在不新增硬件设备的情况下实现对故障的精准识别与隔离，达到飞控系统安全增强的效果。Abstract:There are three major challenges in inertial data redundancy management for flight control systems: common-mode risk suppression, correct voting on singular faults, and single data available. This paper proposes a fault detection algorithm and a redundancy management architecture based on motion dissimilar monitoring. The core of this architecture lies in establishing the relationships among attitude, angular rate, load factor, and body-axis acceleration through engineering-oriented soft-reconfiguration calculations based on simplified motion models. This enables effective fault detection in inertial data and enhances the safety of the flight control system. Real flight test data is used to construct a ground-based offline flight test verification platform, and flight data gathered under boundary flight conditions is used to evaluate the proposed algorithm. The results show that under normal inertial data conditions, the body-axis overload calculated theoretically based on the aircraft dynamics is highly consistent with the actual measured overload, with a maximum deviation of less than 0.1
g . However, there is a noticeable difference between the measured and theoretically computed body-axis overload when any of the angular rate, overload, or attitude signals in the inertial data are faulty. This allows for precise fault identification and isolation without the addition of new hardware, effectively enhancing the safety of the flight control system. -
图 1 引入运动非相似比较监控的余度管理架构
Figure 1. Redundancy management architecture with kinetic-motion dissimilar comparison monitoring introduced
图 2 动力学运动关系约束下的过载重构计算方案
Figure 2. Scheme of load factor reconstruction calculation under kinematic motion relationship constraints
图 4 地面离线试飞验证平台示意图
Figure 4. Schematic diagram of ground offline flight verification platform
图 5 大迎角试飞数据惯导过载信号重构及误差对比结果
Figure 5. Overload signal reconstruction and error comparison in IRU using high angle-of-attack flight test data
图 6 大侧滑试飞数据惯导过载信号重构及误差对比结果
Figure 6. Overload signal reconstruction and error comparison in IRU using large sideslip flight test data
图 7 法向过载信号故障情况下过载信号重构仿真结果
Figure 7. Simulation results of overload signal reconstruction under normal overload signal fault conditions
图 8 俯仰角故障下惯导过载信号重构仿真结果
Figure 8. Simulation results of overload signal reconstruction in IRU under pitch angle fault
图 9 滚转角速率故障下惯导过载信号重构仿真结果
Figure 9. Simulation results of overload signal reconstruction in IRU under roll angular velocity fault
表 1 惯性数据故障检测算法运行时间
Table 1. Execution time of inertial data fault detection algorithm
算法 WCET/μs 本文故障检测算法 37 基于无迹卡尔曼滤波的故障检测算法 6138 （**）注：（**）表示测试过程中不包含飞控系统其他功能。 
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