Fault Diagnosis of UAV System Base On One-Class Support Vector Machine

Abstract

Given the complex structure and long failure time of the flight automation control system, which affect the aircraft's operational efficiency, a fault diagnosis scheme with a one-class support vector machine(OCSVM) optimized by an ant colony optimization(ACO) is proposed. Firstly, this paper analyses the fault characteristics of flight automation systems and constructs a noise filter. Then, a residual decision algorithm based on an improved support vector machine is proposed to judge the residuals in the case of complex flight control system output coupling. Third, experimental simulation results show that the decision algorithm takes about 0.5s for fault detection at a sampling time of 0.1s, significantly reducing fault detection time and an effective fault detection rate of greater than 90%.