Publications:Consensus self-organized models for fault detection (COSMO)
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| Title | Consensus self-organized models for fault detection (COSMO) |
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| Year | 2011 |
| PublicationType | Journal Paper |
| Journal | Engineering applications of artificial intelligence |
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| DOI | http://dx.doi.org/10.1016/j.engappai.2011.03.002 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:416942 |
| 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. |
