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

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

doi:10.1109/SYSTOL.2016.7739747

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 proceeding › Conference contribution › peer-review

10 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 language	English
Title of host publication	Unknown Host Publication
Publisher	IEEE
Pages	177-182
Number of pages	6
ISBN (Electronic)	978-1-5090-0658-8
ISBN (Print)	9781-5090-0657-1, 978-1-5090-0659-5
DOIs	https://doi.org/10.1109/SYSTOL.2016.7739747
Publication status	Published - 10 Nov 2016
Event	2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol) Barcelona, Spain - Barcelona, Spain Duration: 17 Jun 2016 → …

Conference

Conference	2016 3rd Conference on Control and Fault-Tolerant Systems (SysTol) Barcelona, Spain
Period	17/06/16 → …

Keywords

fault diagnosis
model-based
data-driven
vehicular system

Access to Document

10.1109/SYSTOL.2016.7739747

http://uir.ulster.ac.uk/38284/3/SysTol_16_DJ_MN_EF_MK.pdf

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10 Citations - Sourced from Scopus
1 Conference contribution

Design and Selection of Additional Residuals to Enhance Fault Isolation of a Turbocharged Spark Ignited Engine System
Ng, K. Y., Frisk, E. & Krysander, M., 5 May 2020, 7th International Conference on Control, Decision and Information Technologies (CoDIT’20). IEEE, (7th International Conference on Control, Decision and Information Technologies, CoDIT 2020).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Cite this

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title = "A combined diagnosis system design using model-based and data-driven methods",

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.",

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N2 - 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.

AB - 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.

KW - fault diagnosis

KW - model-based

KW - data-driven

KW - vehicular system

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ER -

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

Abstract

Conference

Keywords

Access to Document

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Design and Selection of Additional Residuals to Enhance Fault Isolation of a Turbocharged Spark Ignited Engine System

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