Abstract
The aircraft control system controls the whole flight movement process. Its fault detection can assist the aircraft PHM system in making decisions and completing the targeted maintenance, which is of great significance to improve the safety and reliability of the aircraft. In this paper, by taking advantage of the strong leaning and intelligent recognition ability and the characteristic of less information required in the negative selection artificial immune system, a fault detection method is proposed for aircraft control system based on negative selection algorithm. Basically, after extracting the fault characteristics from the aircraft flight parameters, the negative selection module is utilized to generate fault detectors to monitor the aircraft control system. Afterward, the hypothesis test is introduced to evaluate the detector coverage more efficiently, and the detector cover area is optimized by applying geometric mathematics in the optimization of the detector center position and radius. The method is verified by simulation of a certain aircraft control system, and the results show that it has a good detection effect on the system faults.
Highlights
The aircraft control system completes flight attitude and trajectory control and achieves specific flight actions, which is closely linked with flight safety
A new approach is presented for the fault detection of aircraft control system with the help of negative selection artificial immune system, and which is verified by real QAR data
The geometric mathematics is utilized to improve the overall coverage of the detector and reduce the coincidence between detectors, as well as the hypothesis test is used
Summary
The aircraft control system completes flight attitude and trajectory control and achieves specific flight actions, which is closely linked with flight safety. Duan and Zhang [3] took the system modeling and FMECA as the theoretical basis to realize the detection, and Cheng [4] analyzed the flight parameter data from QAR and found the flap extracting and retracting time can be applied to realize the flap system fault detection In general, these traditional modelbased fault detection methods are required a large number of observers; the diagnosis process is so complicated and inefficient. Artificial immune system is a new intelligent diagnosis system inspired by biological immune system, which is another intelligent system emerged after neural networks and genetic algorithms [5] It has many enlightening special functions for practical engineering problems, such as pattern recognition, memory, and strong learning [6]. The simulation verification shows that the method can effectively complete the fault detection of the aircraft control system, and the fault detection rate can be 98.7%
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