Sensor fault detection and reconstruction system for commercial aircrafts

Abstract

The aim morphing of this study is to detect and reconstruct a fault in angle-of-attack sensor and pitot probes that are components in commercial aircrafts, without false alarm and no need for additional measurements. Real flight data collected from a local airline was used to design the relevant system. Correlation analysis was performed to select the data related to the angle-of-attack and airspeed. Fault detection and reconstruction were carried out by using Adaptive Neural Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANN), which are machine-learning methods. No false alarm was detected when the fault test following the fault modeling was carried out at 0–1 s range by filtering the residual signal. When the fault was detected, fault reconstruction process was initiated so that system output could be achieved according to estimated sensor data. Instead of using the methods based on hardware redundancy, we designed a new system within the scope of this study.

An Augmented Sliding Mode Control for Fixed-Wing UAVs with External Disturbances and Model Uncertainties

“…Firstly, fixed-wing UAVs are always modeled as multistate, highly nonlinear, and state-coupled systems [6]. Furthermore, the modeling of UAVs cannot be entirely accurate due to a variety of factors, including controller delay, sensor errors [7], rigid body assumptions, etc. In addition, wind disturbances, especially sudden wind disturbances, can be safety threats for UAVs that are lightweight and have low moments of inertia.…”