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Statistical Properties and Design Criterions for AI-Based Fault Isolation
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering.
2008 (English)In: IFAC World Congress,2008, Seoul, Korea: IFAC , 2008Conference paper, Published paper (Refereed)
Abstract [en]

Fault diagnosis in the presence of noise and model errors is of fundamental importance. In the paper, the meaning of fault isolation performance is formalized by using the established notion of coverage and false coverage from the field of statistics. Then formal relations describing the relationship between fault isolation performance and the residual related design parameters are derived. For small faults, the measures coverage and false coverage are not applicable so therefore, a different performance criteria, called sub-coverage, is proposed. The performance of different AI-based fault isolation schemes is evaluated and it is shown that the well known principle of minimal cardinality diagnosis gives a very bad performance. Finally, some general design guidelines that guarantee and maximize the fault isolation performance are proposed.

Place, publisher, year, edition, pages
Seoul, Korea: IFAC , 2008.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-44245Local ID: 76108OAI: oai:DiVA.org:liu-44245DiVA: diva2:265107
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2009-12-08

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Nyberg, MattiasKrysander, Mattias

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The Institute of TechnologyDepartment of Electrical Engineering
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