Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/SoSE50414.2020.9130511 | 2020 IEEE 15th International Conference of System of Systems Engineering (SoSE) |
Keywords | DocType | ISBN |
Cloud Computing,System of Systems,Fault Diagnostics,LSTM,Ensemble Learning | Conference | 978-1-7281-8050-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Conor Wallace | 1 | 0 | 0.34 |
| Sean Ackels | 2 | 1 | 1.71 |
| Patrick Benavidez | 3 | 48 | 11.46 |
| Jamshidi, M. | 4 | 30 | 6.75 |