A fractional Fourier transform-based method to detect impacts between the bogie and the car body of a railway vehicle: A data-driven approach
Authors:
- Ricardo Enrique Gutiérrez-Carvajal,
- German R. Betancur,
- Leonel F. Castañeda,
- Grzegorz Zając
Abstract
Structural railway transport elements are typically designed to work for at least 30 years without undergoing major maintenance. However, real-life operational conditions present behaviors different to the model predicted during the initial design phase, which affects the lifetime of the elements in question. This is the case of first-generation railway vehicles which operates in the city of Medellín, Colombia, as the bolster beam presented cracks after 12 years of operation, possibly due to undesired impacts between the bogie and the pivot of the bolster beam. Monitoring vibrational signals would give some sort of an insight into impact phenomena; however, herein lies the problem, as they are difficult to identify using only vibration signals, occurring during time events that take place in a speed-varying system. In this article, the authors present a technique that automatically detects impacts using multiple in-between time/frequency representations, ranking them according to their capacity to discriminate between impact events. Our results show that the best representation for this data was the Fractional Cepstrum Transform at order 0.5 (auROC = 0.961), which outperformed the best pure domain descriptor by least 4%.
- Record ID
- CUTf603b1b32b5e451593488d9b1e062916
- Publication categories
- ;
- Author
- Journal series
- Proceedings of the Institution of Mechanical Engineers Part F-Journal of Rail and Rapid Transit, ISSN 0954-4097, e-ISSN 2041-3017
- Issue year
- 2018
- Vol
- 232
- No
- 1
- Pages
- 288-296
- Article number
- 675187
- Other elements of collation
- il.; Oznaczenie streszczenia - Abstr.; Data udostępnienia on-line - 2016-10-28; Numeracja w czasopiśmie - Vol. 232, Iss. 1
- Keywords in English
- railway vehicle, research and development, superstructure, vibration, fractional Fourier transform, vehicle structure monitoring, machine learning
- DOI
- DOI:10.1177/0954409716675187 Opening in a new tab
- URL
- http://pif.sagepub.com/content/early/2016/10/28/0954409716675187 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 25
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTf603b1b32b5e451593488d9b1e062916/
- URN
urn:pkr-prod:CUTf603b1b32b5e451593488d9b1e062916
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.