【摘要】 为解决一种先进的新型机载传感器——嵌入式大气数据传感器(flush air data sensing,FADS)的故障诊断问题,提出了一种新的方法。基于CFD软件和空气动力学知识获得数据库并建立高精度FADS模型。以系统数学模型为基础,经过严格的公式推导得到故障情况下各组信号的分布特点。为了降低虚警率,基于统计学知识设计了告警次数阈值。为了验证新提出方法的有效性,在不同方差的测量噪声情况下分别将所提方法与以往该领域中被广泛采纳的基于奇偶方程和卡方χ~2分布的2种传统方法进行了对比与分析。结果表明,与以往FADS系统的故障诊断方法相比,新提出方法具有更高的诊断精度和更强的抗干扰性。更多还原

【Abstract】 In order to solve the fault detection problem of flush air data sensing(FADS), an advanced airborne sensor, a new method is proposed in this paper. First, the high-precision FADS model is established on the basis of the database obtained from the CFD software and aerodynamics knowledge. Then, the distribution characteristics of each group of signals under fault condition are derived through strict formulas. Meanwhile, the threshold of alarm times is designed with statistical knowledge. For verifying the effectiveness of the newly proposed method, a comparison with other two widely adopted methods, including the methods based on parity equation and Chi-square χ~2 distribution, is conducted under different measurement noise. Simulation results show that the proposed fault detection method for FADS possess higher accuracy and stronger anti-interference.更多还原