This research presents a novel framework for automated fault detection in cyber-physical systems, with specific focus on large-scale vehicle networks. Agents in a network develop system identification models of themselves which are sent to a local or global authority. The authority excites the system models and generates a fixed-size vector for each one using an echo state network coupled with an autoencoder. The resultant vectors are grouped using standard clustering algorithms, with each group representing similar system model responses. A human expert labels each group once, so that any new group members can be can be associated with the group label. The largest group is assumed to be operating nominally, with all other groups representing a fault or off-nominal operation. We apply our framework to a detailed vehicle cooling system model to demonstrate its efficacy.
A Framework for Automated Collaborative Fault Detection in Large-Scale Vehicle Networks
2019 IEEE Intelligent Vehicles Symposium (IV) ; 1923-1927
2019-06-01
542003 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Collaborative Fault Detection in Resilient Navigation
Springer Verlag | 2023
|Large-Scale Automated Analysis of Vehicle Interactions and Collisions
Online Contents | 2010
|Collaborative Design for Automated DNA Storage That Allows for Rapid, Accurate, Large-Scale Studies
British Library Online Contents | 2004
|