A combined diagnosis system design using model-based and data-driven methods

Daniel Jung, Kok Ng Ng, Erik Frisk, Mattias Krysander

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

A hybrid diagnosis system design is proposed that combines model-based and data-driven diagnosis methods for fault isolation. A set of residuals are used to detect if there is a fault in the system and a consistency-based fault isolation algorithm is used to compute all diagnosis candidates that can explain the triggered residuals. To improve fault isolation, diagnosis candidates are ranked by evaluating the residuals using a set of one-class support vector machines trained using data from different faults. The proposed diagnosis system design is evaluated using simulations of a model describing the air-flow in an internal combustion engine.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages177-182
Number of pages6
ISBN (Electronic)978-1-5090-0658-8
ISBN (Print)9781-5090-0657-1, 978-1-5090-0659-5
DOIs
Publication statusPublished (in print/issue) - 10 Nov 2016
Event2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol) Barcelona, Spain - Barcelona, Spain
Duration: 17 Jun 2016 → …

Conference

Conference2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol) Barcelona, Spain
Period17/06/16 → …

Keywords

  • fault diagnosis
  • model-based
  • data-driven
  • vehicular system

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