Publications:Networked vehicles for automated fault detection
From ISLAB/CAISR
| Title | Networked vehicles for automated fault detection |
|---|---|
| Author | Stefan Byttner and Thorsteinn Rögnvaldsson and Magnus Svensson and George Bitar and Wesley Chominsky |
| Year | 2009 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | 2009 IEEE International Symposium on Circuits and Systems : circuits and systems for human centric smart living technologies, conference program, Taipei International Convention Center, Taipei, Taiwan, May 24-May 27, 2009 |
| Conference | 2009 International Symposium on Circuits and Systems, May 24-27, Taipei, Taiwan |
| DOI | http://dx.doi.org/10.1109/ISCAS.2009.5117980 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:327077 |
| Abstract | Creating fault detection software for complex mechatronic systems (e.g. modern vehicles) is costly both in terms of engineer time and hardware resources. With the availability of wireless communication in vehicles, information can be transmitted from vehicles to allow historical or fleet comparisons. New networked applications can be created that, e.g., monitor if the behavior of a certain system in a vehicle deviates compared to the system behavior observed in a fleet. This allows a new approach to fault detection that can help reduce development costs of fault detection software and create vehicle individual service planning. The COSMO (consensus self-organized modeling) methodology described in this paper creates a compact representation of the data observed for a subsystem or component in a vehicle. A representation that can be sent to a server in a backoffice and compared to similar representations for other vehicles. The backoffice server can collect representations from a single vehicle over time or from a fleet of vehicles to define a norm of the vehicle condition. The vehicle condition can then be monitored, looking for deviations from the norm. The method is demonstrated for measurements made on a real truck driven in varied conditions with ten different generated faults. The proposed method is able to detect all cases without prior information on what a fault looks like or which signals to use. |
