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Multiple-Model-Based Diagnosis of Multiple Faults With High-Speed Train Applications Using Second-Level Adaptation
- Source :
- IEEE Transactions on Industrial Electronics. 68:6257-6266
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Due to the time-varying characteristics and the interacted nature of multiple faults in the high-speed train (HST), the fault modeling, isolation, and severity estimation cannot be described accurately using a single model, which may result in poor performance of the conventional fault diagnosis methods. This article introduces the idea of multiple models and second-level adaptation techniques to diagnose multiple faults of the HST traction motor. First, a reduced model description for the multiple faults is given. Then, a multiple fault isolation framework is developed to simplify the time-varying fault parameters space segmentation. Based on the decoupled fault set, a fault estimation scheme with second-level adaptation is used to provide a reliable alarm priority for different fault scenarios. A case study is performed to verify the effectiveness of the proposed approach.
- Subjects :
- Scheme (programming language)
Computer science
020208 electrical & electronic engineering
Feature extraction
Real-time computing
Hardware_PERFORMANCEANDRELIABILITY
02 engineering and technology
Fault (power engineering)
Traction motor
Set (abstract data type)
Computer Science::Hardware Architecture
Control and Systems Engineering
0202 electrical engineering, electronic engineering, information engineering
Isolation (database systems)
Electrical and Electronic Engineering
Adaptation (computer science)
Computer Science::Operating Systems
computer
Computer Science::Distributed, Parallel, and Cluster Computing
computer.programming_language
Subjects
Details
- ISSN :
- 15579948 and 02780046
- Volume :
- 68
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Industrial Electronics
- Accession number :
- edsair.doi...........fa88258545e6ffac216faa9424a7a6aa
- Full Text :
- https://doi.org/10.1109/tie.2020.2994867