Publications:A Self-Organized Fault Detection Method for Vehicle Fleets
Property "Publisher" has a restricted application area and cannot be used as annotation property by a user.Property "Supervisors" (as page type) with input value "Rögnvaldsson, Thorsteinn (denni), Professor (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), ;;CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650))Nowaczyk, Sławomir (slanow), Associate Professor (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), ;;CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650))" contains invalid characters or is incomplete and therefore can cause unexpected results during a query or annotation process. Property "Author" has a restricted application area and cannot be used as annotation property by a user.
| Title | A Self-Organized Fault Detection Method for Vehicle Fleets |
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| Year | 2016 |
| PublicationType | Licentiate Thesis |
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| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1049704 |
| Abstract | A fleet of commercial heavy-duty vehicles is a very interesting application arena for fault detection and predictive maintenance. With a highly digitized electronic system and hundreds of sensors mounted on-board a modern bus, a huge amount of data is generated from daily operations. This thesis and appended papers present a study of an autonomous framework for fault detection, using the data gathered from the regular operation of vehicles. We employed an unsupervised deviation detection method, called Consensus Self-Organising Models (COSMO), which is based on the concept of ‘wisdom of the crowd’. It assumes that the majority of the group is ‘healthy’; by comparing individual units within the group, deviations from the majority can be considered as potentially ‘faulty’. Information regarding detected anomalies can be utilized to prevent unplanned stops. This thesis demonstrates how knowledge useful for detecting faults and predicting failures can be autonomously generated based on the COSMO method, using different generic data representations. The case study in this work focuses on vehicle air system problems of a commercial fleet of city buses. We propose an approach to evaluate the COSMO method and show that it is capable of detecting various faults and indicates upcoming air compressor failures. A comparison of the proposed method with an expert knowledge based system shows that both methods perform equally well. The thesis also analyses the usage and potential benefits of using the Echo State Network as a generic data representation for the COSMO method and demonstrates the capability of Echo State Network to capture interesting characteristics in detecting different types of faults. |
