A Broad Learning Aided Data-Driven Framework of Fast Fault Diagnosis for High-Speed Trains
Hongtian Chen, Bin Jiang, Steven X. Ding
Abstract
This paper proposes a new fault detection and diagnosis (FDD) architecture for high-speed trains, whose core is a modified broad learning system (BLS). This architecture is a data-driven realization, which enables fast and accurate FDD by effective feature extraction for online implementation. Under the proposed architecture, multiple FDD methods can be developed because of its inherent scalability.
Topics & Concepts
TrainScalabilityComputer scienceFault detection and isolationArchitectureRealization (probability)Fault (geology)Feature extractionReal-time computingFeature (linguistics)Computer architectureEmbedded systemArtificial intelligenceCartographyPhilosophyLinguisticsArtDatabaseActuatorGeologyMathematicsVisual artsSeismologyStatisticsGeographyFault Detection and Control SystemsAdvanced Battery Technologies ResearchMachine Fault Diagnosis Techniques