Real-Time Distributed Ensemble Learning for Fault Detection of an Unmanned Ground Vehicle

Abstract

As the demand for mobile autonomous systems increases across various industries, fault diagnostic systems will need to become more intelligent and robust. In this paper we propose a distributed Long Short-Term Memory (LSTM)- based ensemble learning architecture for learning highly nonlinear, temporal fault classification boundaries for an Unmanned Ground Vehicle (UGV). The main goal of the architecture is to reduce classification bias by ensembling LSTM models as well as achieving near-real time processing time. This is done by parallelizing the deep learning models on Amazon Web Services (AWS) cloud instances via Apache Kafka, a real-time data pipelining infrastructure. An experiment is conducted on a UGV subjected to dislocated suspension faults and results showing the effectiveness of the approach are shown.