A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders  

作　　者：Jorge Magalhães Tomás Jorge Rúben Silva António Guedes Diogo Ribeiro Andreia Meixedo Araliya Mosleh Cecília Vale Pedro Montenegro Alexandre Cury 

机构地区：[1]CONSTRUCT-LESE,School of Engineering,Polytechnic of Porto,Porto,Portugal [2]CONSTRUCT-LESE,Faculty of Engineering,University of Porto,Porto,Portugal [3]Graduate Program in Civil Engineering,Federal University of Juiz de Fora,Juiz de Fora,Brazil

出　　处：《Railway Engineering Science》2024年第4期421-443,共23页铁道工程科学（英文版）

基　　金：a result of project WAY4SafeRail—Wayside monitoring system FOR SAFE RAIL transportation, with reference NORTE-01-0247-FEDER-069595;co-funded by the European Regional Development Fund (ERDF), through the North Portugal Regional Operational Programme (NORTE2020), under the PORTUGAL 2020 Partnership Agreement;financially supported by Base Funding-UIDB/04708/2020;Programmatic Funding-UIDP/04708/2020 of the CONSTRUCT—Instituto de Estruturas e Constru??es;funded by national funds through the FCT/ MCTES (PIDDAC);Grant No. 2021.04272. CEECIND from the Stimulus of Scientific Employment, Individual Support (CEECIND) - 4th Edition provided by “FCT – Funda??o para a Ciência, DOI : https:// doi. org/ 10. 54499/ 2021. 04272. CEECI ND/ CP1679/ CT0003”。

摘　　要：Wayside monitoring is a promising cost-effective alternative to predict damage in the rolling stock. The main goal of this work is to present an unsupervised methodology to identify out-of-roundness(OOR) damage wheels, such as wheel flats and polygonal wheels. This automatic damage identification algorithm is based on the vertical acceleration evaluated on the rails using a virtual wayside monitoring system and involves the application of a two-step procedure. The first step aims to define a confidence boundary by using(healthy) measurements evaluated on the rail constituting a baseline. The second step of the procedure involves classifying damage of predefined scenarios with different levels of severities. The proposed procedure is based on a machine learning methodology and includes the following stages:(1) data collection,(2) damage-sensitive feature extraction from the acquired responses using a neural network model, i.e., the sparse autoencoder(SAE),(3) data fusion based on the Mahalanobis distance, and(4) unsupervised feature classification by implementing outlier and cluster analysis. This procedure considers baseline responses at different speeds and rail irregularities to train the SAE model. Then, the trained SAE is capable to reconstruct test responses(not trained) allowing to compute the accumulative difference between original and reconstructed signals. The results prove the efficiency of the proposed approach in identifying the two most common types of OOR in railway wheels.

关 键 词：OOR wheel damage Damage identification Sparse autoencoder Passenger trains Wayside condition monitoring 

分 类 号：U279[机械工程—车辆工程]