SVM-based approach for instrument fault accomodation in automotive systems

Author

Betta, Giovanni ; Bernieri, Andrea ; Capriglione, Domenico ; Molinara, Mario

Author_Institution

DAEIMI, Cassino Univ., Italy

fYear

2005

fDate

18-20 July 2005

Abstract

The paper deals with the use of support vector machines (SVMs) in software-based instrument fault accommodation schemes. A performance comparisons between SVMs and artificial neural networks (ANNs) is also reported. As an example, a real case study on an automotive system is presented. The ANNs and SVMs regression capability are employed to accommodate faults that could occur on main sensors involved in the engine operating. The obtained results prove the good behaviour of both tools. Similar performances have been achieved in terms of accuracy.

Keywords

automotive electronics; computerised instrumentation; fault diagnosis; neural nets; support vector machines; artificial neural networks; automotive systems; engine operation; software-based instrument fault accommodation; support vector machines; Artificial neural networks; Automotive engineering; Engines; Fault detection; Fault diagnosis; Instruments; Pollution measurement; Sensor systems; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Virtual Environments, Human-Computer Interfaces and Measurement Systems, 2005. VECIMS 2005. Proceedings of the 2005 IEEE International Conference on

Print_ISBN

0-7803-9041-5

Type

conf

DOI

10.1109/VECIMS.2005.1567582

Filename

1567582