传统的舵机故障检测通常基于舵机参数已知,但实际中大多数舵机的内部参数是未知的。文中采用搭建故障观测器的思路,提出了一种基于子空间辨识的卡尔曼滤波故障检测方法;借助于dSPACE半实物仿真工具,搭建了电动舵机故障检测半实物仿真平台,实现了未知参数电动舵机的在线故障检测和快速故障分析。实验表明,该方法不但能有效检测故障发生时间,而且能快速辩识故障类型和程度,为无人机舵机故障快速检测的工程化应用提供了一种解决方案。

The traditional steering engine fault detection was usually based on the known parameters of steering engine, but in practice, most of the internal parameters of the steering engine were unknown. A Kalman filter for filtering fault detection method based on subspace identification was proposed with the thought of establishing fault observer in this paper. At the same time, based on hardware-in-the-loop simulation tool dSPACE, a semi-phisical simulation platform for electric steering engine fault detection was set up, through which the fault on-line detection and fast fault analysis of unknown-parameters steering engine were realized. The experimental results showed that this method could not only detect the fault occurence time effectively, but also identify the fault types and degree quickly, and the achievement provided a solution for the engineering application rapid fault detection of steering engine in UAV.