Computational Technology Resources - CCC

A. Mosleh¹, A. Meixedo¹, D. Ribeiro², P. Montenegro¹ and R. Calcada¹

¹CONSTRUCT – LESE, Faculty of Engineering, University of Porto, Portugal
²CONSTRUCT – LESE, School of Engineering, Polytechnic of Porto, Porto, Portugal

doi:10.4203/ccc.1.21.22

A. Mosleh, A. Meixedo, D. Ribeiro, P. Montenegro, R. Calcada, "Intelligent clustering-based approach for railway wheel flat detection", in J. Pombo, (Editor), "Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 1, Paper 21.22, 2022, doi:10.4203/ccc.1.21.22

Keywords: machine learning, damage identification, railway wheel flat, wayside condition monitoring.

Abstract

The main goal of this paper is to present an unsupervised methodology to identify railway wheel flats. This automatic damage identification algorithm is based on the acceleration evaluated on the rails for the passage of traffic loads and deals with the application of a two-step procedure. The first step aims to build a confidence boundary using baseline responses evaluated from the rail, while the second step involves the damages’ classification based on different severities levels. The proposed procedure is based on a machine learning methodology and involves the following steps: (i) data acquisition from sensors, (ii) feature extraction from acquired responses using an AR model, (iii) feature normalization using principal component analysis, (iv) data fusion and (v) unsupervised feature classification by implementing outlier and cluster analyses. The obtained results show that the proposed methodology is a reliable and cost-effective method that can be successfully used for the wheel flats identifications and considering different operating speeds

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 1 PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 21.22 Intelligent clustering-based approach for railway wheel flat detection A. Mosleh¹, A. Meixedo¹, D. Ribeiro², P. Montenegro¹ and R. Calcada¹ ¹CONSTRUCT – LESE, Faculty of Engineering, University of Porto, Portugal ²CONSTRUCT – LESE, School of Engineering, Polytechnic of Porto, Porto, Portugal doi:10.4203/ccc.1.21.22 Full Bibliographic Reference for this paper A. Mosleh, A. Meixedo, D. Ribeiro, P. Montenegro, R. Calcada, "Intelligent clustering-based approach for railway wheel flat detection", in J. Pombo, (Editor), "Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 1, Paper 21.22, 2022, doi:10.4203/ccc.1.21.22 Keywords: machine learning, damage identification, railway wheel flat, wayside condition monitoring. Abstract The main goal of this paper is to present an unsupervised methodology to identify railway wheel flats. This automatic damage identification algorithm is based on the acceleration evaluated on the rails for the passage of traffic loads and deals with the application of a two-step procedure. The first step aims to build a confidence boundary using baseline responses evaluated from the rail, while the second step involves the damages’ classification based on different severities levels. The proposed procedure is based on a machine learning methodology and involves the following steps: (i) data acquisition from sensors, (ii) feature extraction from acquired responses using an AR model, (iii) feature normalization using principal component analysis, (iv) data fusion and (v) unsupervised feature classification by implementing outlier and cluster analyses. The obtained results show that the proposed methodology is a reliable and cost-effective method that can be successfully used for the wheel flats identifications and considering different operating speeds download the full-text of this paper (PDF, 1268 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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