A State Monitoring Algorithm for Data Missing Scenarios via Convolutional Neural Network and Random Forest

Abstract

In Unmanned Aerial Vehicle (UAV) systems, packet loss during sensor data transmission causes data missing, which reduces fault features in sensor signals and causes the accuracy of state monitoring to decrease. This study proposes a state monitoring algorithm combining a convolutional neural network (CNN) with a random forest (RF) for data missing scenarios. CNN algorithm is designed to extract the distributed fault information from the available signals and acquire the state features of the system. Random forest algorithm processes the state features and judges the system state. The integrating strategy utilizes the automatic feature extraction capability of CNN and the superior discrimination capability of an RF classifier to improve the state monitoring accuracy. The experimental results show that the accuracy of state monitoring in data missing condition reaches 92.74%. The comparative experiments verify the validity of the proposed algorithm.

Keywords

• State monitoring
• data missing
• deep learning
• convolutional neural network
• random forest