Fault Detection and Identification on UAV System with CITFA Algorithm Based on Deep Learning

Abstract

In this paper, a new algorithm for detecting and identifying faults in a UAV system is proposed, this algorithm uses Color Images obtained from Time-Frequency-Amplitude (CITFA) graphs for faults classification. The most important innovations of CITFA algorithm are, image based processing and classification using deep neural network. In most systems, faults can cause irreparable costs. For this reason, detecting and identifying faults is one of the most important issues, today. In this paper, a variety of sensor and actuator faults are investigated. The paper focus is mostly on deep learning and time-frequency graphs. The selected system for fault detection and proposed algorithm implementation is a UAV system. After designing the Linear Quadratic Regulator (LQR) controller for the system, a variety of faulty signals are made. Using the proposed algorithm, these signals are converted to images. Finally, these images are classified using the proposed algorithm, based on deep learning. The test signals are classified into five types of faults with the accuracy of 98%.