Severity-based diagnosis for vehicular electric systems with multiple, interacting fault modes
Complex systems are comprised of multiple components that continuously interact in terms of how they degrade and fail. Diagnosing fault severity and causes of failures in these systems is often a non-trivial task. To address this challenge, the authors propose a data-driven, severity-based diagnosis framework for systems with multiple, interacting fault modes. They focus on the components of the automotive electric power generation and storage system, specifically, the Vehicle-Engine Start system comprised of the battery and the start-stop starter. This framework leverages sensor data from several component-fault severity combinations. Using multiple feature extraction tools, the authors train separate classifiers using Regularized Multinomial Regression, and combine the performance of the classifiers using ensemble methods. They demonstrate the effectiveness of their approach by performing degradation-based diagnostic tests utilizing a real-world engine test-rig.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/09518320
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Supplemental Notes:
- © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
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Authors:
- Peters, Benjamin
- Yildirim, Murat
- Gebraeel, Nagi
- Paynabar, Kamran
- Publication Date: 2020-3
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; Photos; References; Tables;
- Pagination: 106605
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Serial:
- Reliability Engineering & System Safety
- Volume: 195
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0951-8320
- Serial URL: https://www.sciencedirect.com/journal/reliability-engineering-and-system-safety
Subject/Index Terms
- TRT Terms: Diagnostic tests; Engine starters; Fault location; In vehicle sensors; Machine learning; Vehicle electrical systems
- Subject Areas: Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01845825
- Record Type: Publication
- Files: TRIS
- Created Date: May 19 2022 10:41AM