Electronics letters, 2014-05, Vol.50 (11), p.824-826
2014
Volltextzugriff (PDF)
Details
- Autor(en) / Beteiligte
- Titel
- Fault diagnosis in fuel cell systems using quantitative models and support vector machines
- Ist Teil von
- Electronics letters, 2014-05, Vol.50 (11), p.824-826
- Ort / Verlag
- Stevenage: The Institution of Engineering and Technology
- Erscheinungsjahr
- 2014
- Quelle
- EZB Electronic Journals Library
- Beschreibungen/Notizen
- Fault detection and identification are new and challenging tasks for electrical generation plants that are based on solid oxide fuel cells. The use of a quantitative model of the plant together with a support vector machine to design and operate a supervised classification system is proposed. This type of system, which uses a few easy-to-measure features selected through the maximisation of a classification error bound, proved to be effective in revealing a faulty condition and identifying it among the four considered fault classes.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0013-5194, 1350-911XeISSN: 1350-911XDOI: 10.1049/el.2014.0565Titel-ID: cdi_pascalfrancis_primary_28496022
- Format
- –
- Schlagworte
- Applied sciences, Classification, classification error bound maximisation, Computer science, control theory, systems, Data processing. List processing. Character string processing, Design engineering, easy‐to‐measure features, electrical generation plants, Energy, Energy. Thermal use of fuels, Equipments for energy generation and conversion: thermal, electrical, mechanical energy, etc, Exact sciences and technology, fault class, Fault detection, fault diagnosis, fault identification, Faults, faulty condition, fuel cell power plants, fuel cell systems, Fuel cells, Industrial metrology. Testing, Mechanical engineering. Machine design, Memory organisation. Data processing, Power electronics, energy conversion and sustainability, power engineering computing, Power plants, quantitative model, Software, Solid oxide fuel cells, supervised classification system, Support vector machines, Tasks