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...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.展开更多
Dynamic wheel-rail contact forces induced by a severe form of wheel tread damage have been measured by a wheel impact load detector during full-scale field tests at different vehicle speeds.Based on laser scanning,the...Dynamic wheel-rail contact forces induced by a severe form of wheel tread damage have been measured by a wheel impact load detector during full-scale field tests at different vehicle speeds.Based on laser scanning,the measured three-dimensional damage geometry is employed in simulations of dynamic vehicle-track interaction to calibrate and verify a simulation model.The relation between the magnitude of the impact load and various operational parameters,such as vehicle speed,lateral position of wheel-rail contact,track stiffness and position of impact within a sleeper bay,is investigated.The calibrated model is later employed in simulations featuring other forms of tread damage;their effects on impact load and subsequent fatigue impact on bearings,wheel webs and subsurface initiated rolling contact fatigue of the wheel tread are assessed.The results quantify the effects of wheel tread defects and are valuable in a shift towards condition-based maintenance of running gear,and for general assessment of the severity of different types of railway wheel tread damage.展开更多
Thermal or thermo-mechanical loading is one of the major causes of wheel surface damage in Australian heavy haul operations.In addition,multi-wear wheels appear to be particularly sensitive to thermo-mechanical damage...Thermal or thermo-mechanical loading is one of the major causes of wheel surface damage in Australian heavy haul operations.In addition,multi-wear wheels appear to be particularly sensitive to thermo-mechanical damage during their first service life.Such damage can incur heavy machining penalties or even premature scrapping of wheels.The combination of high contact stresses as well as substantial thermal loading(such as during prolonged periods of tread braking) can lead to severe plastic deformation,thermal fatigue and microstructural deterioration.For some high-strength wheel grades,the increased sensitivity to thermo-mechanical damage observed during the first service period may be attributed to the presence of a near-surface region in which the microstructure is more sensitive to these loading conditions than the underlying material.The standards applicable to wheels used in Australian heavy haul operations are based on the Association of American Railroads(AAR) specification M-107/M-208,which does not include any requirements for microstructure.The implementation of acceptance criteria for the microstructure,in particular that in the near-surface region of the wheel,may be necessary when new wheels are purchased.The stability of wheel microstructures during thermo-mechanical loading and the effects of alloying elements commonly used in wheel manufacturing are reviewed.A brief guide to improving thermal/mechanical stability of the microstructure is also provided.展开更多
基金a result of project WAY4SafeRail—Wayside monitoring system FOR SAFE RAIL transportation, with reference NORTE-01-0247-FEDER-069595co-funded by the European Regional Development Fund (ERDF), through the North Portugal Regional Operational Programme (NORTE2020), under the PORTUGAL 2020 Partnership Agreement+3 种基金financially supported by Base Funding-UIDB/04708/2020Programmatic Funding-UIDP/04708/2020 of the CONSTRUCT—Instituto de Estruturas e Constru??esfunded 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”。
文摘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.
基金funded from the European Union's Horizon 2020 research and innovation programme in the project In2Track3 under grant agreement No.101012456.
文摘Dynamic wheel-rail contact forces induced by a severe form of wheel tread damage have been measured by a wheel impact load detector during full-scale field tests at different vehicle speeds.Based on laser scanning,the measured three-dimensional damage geometry is employed in simulations of dynamic vehicle-track interaction to calibrate and verify a simulation model.The relation between the magnitude of the impact load and various operational parameters,such as vehicle speed,lateral position of wheel-rail contact,track stiffness and position of impact within a sleeper bay,is investigated.The calibrated model is later employed in simulations featuring other forms of tread damage;their effects on impact load and subsequent fatigue impact on bearings,wheel webs and subsurface initiated rolling contact fatigue of the wheel tread are assessed.The results quantify the effects of wheel tread defects and are valuable in a shift towards condition-based maintenance of running gear,and for general assessment of the severity of different types of railway wheel tread damage.
文摘Thermal or thermo-mechanical loading is one of the major causes of wheel surface damage in Australian heavy haul operations.In addition,multi-wear wheels appear to be particularly sensitive to thermo-mechanical damage during their first service life.Such damage can incur heavy machining penalties or even premature scrapping of wheels.The combination of high contact stresses as well as substantial thermal loading(such as during prolonged periods of tread braking) can lead to severe plastic deformation,thermal fatigue and microstructural deterioration.For some high-strength wheel grades,the increased sensitivity to thermo-mechanical damage observed during the first service period may be attributed to the presence of a near-surface region in which the microstructure is more sensitive to these loading conditions than the underlying material.The standards applicable to wheels used in Australian heavy haul operations are based on the Association of American Railroads(AAR) specification M-107/M-208,which does not include any requirements for microstructure.The implementation of acceptance criteria for the microstructure,in particular that in the near-surface region of the wheel,may be necessary when new wheels are purchased.The stability of wheel microstructures during thermo-mechanical loading and the effects of alloying elements commonly used in wheel manufacturing are reviewed.A brief guide to improving thermal/mechanical stability of the microstructure is also provided.
