Abstract
The accurate prognostics for actuator malfunctions is a challenging task. Developing reliable prognostic methods is vital for providing reasonable preventive maintenance schedules and preventing unexpected failures. Particle filter has been proved to be a traditional approach to deal with actuator prognostic problems. However, the measurement function in the particle filter algorithm cannot be obtained in the prediction process, this paper presents a hybrid framework combining support vector regression (SVR) and particle filter (PF). The SVR output prediction results are employed as the “measurements” for the subsequent PF algorithm. To accomplish the accurate prognostics for actuator fault of civil aircraft, an improved PF based on Kendall correlation coefficient is put forward to solve the problem of particles’ degeneracy. The experimental results are presented, demonstrating that the SVR-PF hybrid approach has satisfactory performance with better prognostics accuracy and higher fault resolution than traditional approaches.
Highlights
Developing a satisfactory fault prognostics technique for the aircraft automatic flight control system (AFCS) is of great importance for the civil aviation safety
A typical model-based fault prognostics approach generally starts with the internal working mechanism of the object system, and predicts the trend of development by using an analytical model that can reflect the physical laws of the system
The proposed support vector regression (SVR)-particle filter (PF) method is described in detail in Section 2, where we present an improved PF algorithm
Summary
Developing a satisfactory fault prognostics technique for the aircraft automatic flight control system (AFCS) is of great importance for the civil aviation safety. A typical model-based fault prognostics approach generally starts with the internal working mechanism of the object system, and predicts the trend of development by using an analytical model that can reflect the physical laws of the system. The main contributions of this paper lie in two aspects: 1) From the perspective of datadriven approach, a novel framework combining SVR and PF is proposed to accurately estimate the state variables inside the actuator that are not visible (can’t be directly measured), while is capable of achieving fault prognostics based on state variables with higher resolution; 2) An improved method for PF is developed to alleviate the problem of particle degeneracy to achieve better prediction accuracy.
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