Consensus self-organized models for fault detection (COSMO). Byttner, S., Rögnvaldsson, T., & Svensson, M. Engineering Applications of Artificial Intelligence, 24(5):833–839, August, 2011. ![Consensus self-organized models for fault detection (COSMO) [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Methods for equipment monitoring are traditionally constructed from specific sensors and/or knowledge collected prior to implementation on the equipment. A different approach is presented here that builds up knowledge over time by exploratory search among the signals available on the internal field bus system and comparing the observed signal relationships among a group of equipment that perform similar tasks. The approach is developed for the purpose of increasing vehicle uptime, and is therefore demonstrated in the case of a city bus and a heavy duty truck. However, it also works fine for smaller mechatronic systems like computer hard-drives. The approach builds on an onboard self-organized search for models that capture relations among signal values on the vehicles' data buses, combined with a limited bandwidth telematics gateway and an off-line server application where the parameters of the self-organized models are compared. The presented approach represents a new look at error detection in commercial mechatronic systems, where the normal behavior of a system is actually found under real operating conditions, rather than the behavior observed in a number of laboratory tests or test-drives prior to production of the system. The approach has potential to be the basis for a self-discovering system for general purpose fault detection and diagnostics.
@article{byttner_consensus_2011,
title = {Consensus self-organized models for fault detection ({COSMO})},
volume = {24},
issn = {0952-1976},
url = {https://www.sciencedirect.com/science/article/pii/S0952197611000467},
doi = {10.1016/j.engappai.2011.03.002},
abstract = {Methods for equipment monitoring are traditionally constructed from specific sensors and/or knowledge collected prior to implementation on the equipment. A different approach is presented here that builds up knowledge over time by exploratory search among the signals available on the internal field bus system and comparing the observed signal relationships among a group of equipment that perform similar tasks. The approach is developed for the purpose of increasing vehicle uptime, and is therefore demonstrated in the case of a city bus and a heavy duty truck. However, it also works fine for smaller mechatronic systems like computer hard-drives. The approach builds on an onboard self-organized search for models that capture relations among signal values on the vehicles' data buses, combined with a limited bandwidth telematics gateway and an off-line server application where the parameters of the self-organized models are compared. The presented approach represents a new look at error detection in commercial mechatronic systems, where the normal behavior of a system is actually found under real operating conditions, rather than the behavior observed in a number of laboratory tests or test-drives prior to production of the system. The approach has potential to be the basis for a self-discovering system for general purpose fault detection and diagnostics.},
language = {en},
number = {5},
urldate = {2023-02-13},
journal = {Engineering Applications of Artificial Intelligence},
author = {Byttner, S. and Rögnvaldsson, T. and Svensson, M.},
month = aug,
year = {2011},
keywords = {Fault detection, Fleet management, Remote maintenance, Self-organizing systems, Telematics},
pages = {833--839},
}
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