Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Automatic clustering-based approach for train wheels condition monitoring
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 severity 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. To evaluate whether the number of sensors used to detect and classify wheel flat can be optimized, the influence of sensors’ number is performed.
Automatic clustering-based approach for train wheels condition monitoring
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 severity 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. To evaluate whether the number of sensors used to detect and classify wheel flat can be optimized, the influence of sensors’ number is performed.
Automatic clustering-based approach for train wheels condition monitoring
Mosleh, Araliya (Autor:in) / Meixedo, Andreia (Autor:in) / Ribeiro, Diogo (Autor:in) / Montenegro, Pedro (Autor:in) / Calçada, Rui (Autor:in)
International Journal of Rail Transportation ; 11 ; 639-664
03.09.2023
26 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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