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Data-Driven Incipient Sensor Fault Estimation with Application in Inverter of High-Speed Railway
Joint Authors
Lu, Ningyun
Chen, Hongtian
Jiang, Bin
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-09-10
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Incipient faults in high-speed railway have been rarely considered before developing into faults or failures.
In this paper, a new data-driven incipient fault estimate (FE) methodology is proposed under multivariate statistics frame, which incorporates with Kullback-Leibler divergence (KLD) in information domain and neural network approximation in machine learning.
By defining one sensitive fault indicator (SFI), the incipient fault amplitude can be precisely estimated.
According to the experimental platform of China Railway High-speed 2 (CRH2), the proposed incipient FE algorithm is examined, and the more sensitivity and accuracy to tiny abnormality are demonstrated.
Followed by the incipient FE results, several factors on FE performance are further analyzed.
American Psychological Association (APA)
Chen, Hongtian& Jiang, Bin& Lu, Ningyun. 2017. Data-Driven Incipient Sensor Fault Estimation with Application in Inverter of High-Speed Railway. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1192524
Modern Language Association (MLA)
Chen, Hongtian…[et al.]. Data-Driven Incipient Sensor Fault Estimation with Application in Inverter of High-Speed Railway. Mathematical Problems in Engineering No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1192524
American Medical Association (AMA)
Chen, Hongtian& Jiang, Bin& Lu, Ningyun. Data-Driven Incipient Sensor Fault Estimation with Application in Inverter of High-Speed Railway. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1192524
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192524