Using MLSTM and Multioutput Convolutional LSTM Algorithms for Detecting Anomalous Patterns in Streamed Data of Unmanned Aerial Vehicles

Abstract

In this article, we present a comparative study of two existing deep learning tools that are used in a novel way to detect anomalies in the streamed data of the unmanned aerial vehicle (UAV). Detecting anomalies is very vital to predict potential faults that are caused by hardware and software faults and may prevent the UAV from hazardous accidents. Therefore, we suggest using multiple long short-term memory and multioutput convolutional long short-term memory (LSTM) to detect anomalies in UAV data. LSTM networks attracted many researchers in several domains, as it is a useful tool for learning dynamic temporal patterns and long-range dependencies in sequential data, which cannot be achieved using traditional neural networks. However, utilizing multiple LSTM networks would result in too much redundancy. The redundancy issue can be solved by incorporating one convolutional LSTM (ConvLSTM) network with multiple outputs. The ConvLSTM is suitable for analyzing multivariate temporal data; due to its convolutional architecture and the advantage of preserving the benefits of the LSTM networks. We evaluated and compared the two approaches using well-known indicators such as the detection rate, the false alarm rate, the precision, and the F.score indicators. The two methods exhibited promising results in predicting different types of faults, for instance (sensor-impulse and sensor-cut). However, the multioutput ConvLSTM was faster in training and testing phases, and its results were superior in predicting (sensor-stuck and sensor-drift) faults.