Skip to Main content Skip to Navigation
Journal articles

A SVM framework for fault detection of the braking system in a high speed train

Abstract : In April 2015, the number of operating High Speed Trains (HSTs) in the world has reached 3603. An efficient, effective and very reliable braking system is evidently very critical for trains running at a speed around 300 km/h. Failure of a highly reliable braking system is a rare event and, consequently, informative recorded data on fault conditions are scarce. This renders the fault detection problem a classification problem with highly unbalanced data. In this paper, a Support Vector Machine (SVM) framework, including feature selection, feature vector selection, model construction and decision boundary optimization, is proposed for tackling this problem. Feature vector selection can largely reduce the data size and, thus, the computational burden. The constructed model is a modified version of the least square SVM, in which a higher cost is assigned to the error of classification of faulty conditions than the error of classification of normal conditions. The proposed framework is successfully validated on a number of public unbalanced datasets. Then, it is applied for the fault detection of braking systems in HST: in comparison with several SVM approaches for unbalanced datasets, the proposed framework gives better results.
Document type :
Journal articles
Complete list of metadatas

Cited literature [28 references]  Display  Hide  Download

https://hal-centralesupelec.archives-ouvertes.fr/hal-01408781
Contributor : Jie Liu <>
Submitted on : Friday, March 20, 2020 - 7:15:38 PM
Last modification on : Wednesday, July 15, 2020 - 10:36:13 AM
Long-term archiving on: : Sunday, June 21, 2020 - 4:54:49 PM

File

185_A-SVM-framework-for-fault-...
Files produced by the author(s)

Identifiers

Citation

Jie Liu, Yan-Fu Li, Enrico Zio. A SVM framework for fault detection of the braking system in a high speed train. Mechanical Systems and Signal Processing, Elsevier, 2017, 87, pp.401 - 409. ⟨10.1016/j.ymssp.2016.10.034⟩. ⟨hal-01408781⟩

Share

Metrics

Record views

486

Files downloads

716