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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Automatic clustering-based approach for train wheels condition monitoring


    Contributors:

    Published in:

    Publication date :

    2023-09-03


    Size :

    26 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Development and testing of an automatic remote condition monitoring system for train wheels

    Hyde, Paul / Defossez, Francois / Ulianov, Cristian | IET | 2016

    Free access

    Development and testing of an automatic remote condition monitoring system for train wheels

    Hyde, Paul / Defossez, Francois / Ulianov, Cristian | Wiley | 2016

    Free access

    Condition monitoring transforms train maintenance

    Robertsson, Thoma / Fry, Kevin | IuD Bahn | 2007



    Condition Monitoring of Track/Train Interface

    Nicks, S. / IEE; Railway Industry Group | British Library Conference Proceedings | 1998