Early identification of out-of-roundness damage wheels in railway freight vehicles using a wayside system and a stacked sparse autoencoder

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Jorge, T (Author) Other; Magalhaes, J (Author) Other; Silva, R (Author) Other; Guedes, A (Author) Other; Ribeiro, D (Author) Other; Cecília Vale (Author) FEUP; Meixedo, A (Author) Other; Mosleh, A (Author) FEUP; Pedro Aires Montenegro (Author) FEUP; Cury, A (Author) Other

Journal

Title: Vehicle System Dynamics

Pages: 1-26

ISSN: 0042-3114

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-010-8CZ

DOI: 10.1080/00423114.2024.2333771

Abstract (EN): Early identification of wheel defects can prevent serious damage to railways, considerably lowering maintenance costs for both railway administrations and rolling stock operators. Within this context, an unsupervised methodology based on artificial intelligence techniques is presented, which allows the detection and classification of out-of-roundness damage wheels, such as wheel flats and polygonal wheels, based on dynamic responses induced on the track by crossing freight railway vehicles. The methodology involves the following steps: (i) data collection and pre-processing, (ii) feature extraction (iii) data fusion and (iv) feature discrimination. In the first phase, an FFT algorithm is applied to the acceleration track responses. Then, features are extracted after training a Stacked Sparse Autoencoder, in which the main features of the responses are obtained after a compression stage using an encoder network. This lower dimensional layer forces the model to learn a compression of the input data. Then, these extracted features are merged using the Mahalanobis distance, which enhances the sensitivity to the damage recognition. Posteriorly, an outlier analysis is performed to distinguish a healthy wheel from a defective one and a cluster analysis to discriminate the two types of out-of-roundness (OOR) damage and classify the severity of each type of damage.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 26