Difference between revisions of "Publications:Modelling for Vehicle Fleet Remote Diagnostics"
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| − | |Name=Byttner, Stefan | + | |Name=Byttner, Stefan (stefan) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), Intelligenta system (IS-lab) (3941));Rögnvaldsson, Thorsteinn (denni) (0000-0001-5163-2997) (Högskolan i Halmstad (2804), Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE) (3905), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), Intelligenta system (IS-lab) (3941));Svensson, Magnus (magsveARC13) (Volvo Technology, SE-405 08 Göteborg, Sweden) |
|Title=Modelling for Vehicle Fleet Remote Diagnostics | |Title=Modelling for Vehicle Fleet Remote Diagnostics | ||
|PublicationType=Conference Paper | |PublicationType=Conference Paper | ||
Latest revision as of 21:39, 30 September 2016
| Title | Modelling for Vehicle Fleet Remote Diagnostics |
|---|---|
| Author | Stefan Byttner and Thorsteinn Rögnvaldsson and Magnus Svensson |
| Year | 2007 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | Proceedings of SAE 2007 Commercial Vehicle Engineering Congress |
| Conference | SAE 2007 Commercial Vehicle Engineering Congress & Exhibition, October 2007, Rosemont, IL, USA |
| DOI | http://dx.doi.org/10.4271/2007-01-4154 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:239342 |
| Abstract | Quality and up-time management of vehicles is today receiving much attention from vehicle manufacturers. One of the reasons is that there is a desire to avoiding on-road failures to addressing potential issues during routine maintenance intervals or at times more convenient to the operator. Forthcoming telematic platforms and advanced diagnostic algorithms can enable the possibility to proactively handle problems and minimize stops. The platforms bring the possibility of increasing knowledge of fault characteristics and making diagnostic decisions by using a population of vehicles. However, this requires real-time diagnostic algorithms that process data both onboard and offboard at a central server. The paper presents a self organizing approach for failure and deviation detection on a fleet of vehicles. The approach builds on using parametric models for encoding the characteristical relations between different sensor readings for a vehicle sub-system or component. The models are low-dimensional representations of the operating characteristics of a sub-system or component and are possible to transfer over a limited wireless communication channel. The approach is demonstrated on simulated data of an electronically controlled suspension system for detecting a slow valve and a leaking bellow. |
