Accelerometer Placement Comparison for Crack Detection in Railway Axles Using Vibration Signals and Machine Learning

Pablo Lucero, Réne Vinicio Sánchez, Jean Carlo MacAncela, Diego Cabrera, Mariela Cerrada, Chuan Li, Higinio Rubio Alonso

Research output: Contribution to conferenceChapter

Abstract

In this paper, a methodology for accelerometer placement comparison for crack detection in railway axles, using vibration signals and machine learning, was shown. Different vibration signals from six accelerometers were obtained by several conditions of load and speed, with crack depths in axles from 5.7 to 15 mm. This paper describes three stages: acquisition, processing, and analysis. The findings suggest that using the vertical or longitudinal accelerometer located in left allow obtaining higher accuracy than 90% with three features, also called condition indicators. On the other hand, an accuracy such as 96.43% is obtained using a left vertical sensor and 95,98% using a left longitudinal sensor, both with ten features. With this methodology, high accuracy in crack detection was obtained using an accelerometer effective placement. Different vibration signals using six accelerometers were obtained, under several conditions of load and speed, with crack depths in axles from 5.7 to 15 mm.

Original languageEnglish
Pages291-296
Number of pages6
DOIs
StatePublished - 12 Jul 2019
EventProceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019 -
Duration: 1 May 2019 → …

Conference

ConferenceProceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019
Period1/05/19 → …

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Keywords

  • crack detection
  • feature selection
  • machine learning
  • railway
  • vibration signal

Cite this

Lucero, P., Sánchez, R. V., MacAncela, J. C., Cabrera, D., Cerrada, M., Li, C., & Alonso, H. R. (2019). Accelerometer Placement Comparison for Crack Detection in Railway Axles Using Vibration Signals and Machine Learning. 291-296. Proceedings - 2019 Prognostics and System Health Management Conference, PHM-Paris 2019, . https://doi.org/10.1109/PHM-Paris.2019.00056