TY - JOUR
T1 - Pareto-based maintenance decisions for regional railways with uncertain weld conditions using the Hilbert spectrum of axle box acceleration
AU - Nunez, Alfredo
AU - Jamshidi, Ali
AU - Wang, Hongrui
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2018/6/14
Y1 - 2018/6/14
N2 - This paper presents a Pareto-based maintenance decision system for rail welds in a regional railway network. Weld health condition data are collected using a train in operation. A Hilbert spectrum-based approach is used for data processing to detect and assess the weld quality based on multiple registered dynamic responses in the axle box acceleration measurements. The assessment of the welds is stochastic in nature and variant over time, so a set of robust and predictive key performance indicators is defined to capture the weld degradation dynamics during a given maintenance period. Using a scenario-based approach, two objective functions are defined, performance and the number of weld replacements. Evolutionary multi-objective optimization is employed to optimize the objective functions so that the trade-offs between performance and cost support decision-making for railway network maintenance. The results of the proposed methodology show that the infrastructure manager can localize field inspections and maintenance efforts on the area with the most critical welds. To showcase the capability of the proposed methodology, measurements from a regional railway network in Transylvania, Romania are employed.
AB - This paper presents a Pareto-based maintenance decision system for rail welds in a regional railway network. Weld health condition data are collected using a train in operation. A Hilbert spectrum-based approach is used for data processing to detect and assess the weld quality based on multiple registered dynamic responses in the axle box acceleration measurements. The assessment of the welds is stochastic in nature and variant over time, so a set of robust and predictive key performance indicators is defined to capture the weld degradation dynamics during a given maintenance period. Using a scenario-based approach, two objective functions are defined, performance and the number of weld replacements. Evolutionary multi-objective optimization is employed to optimize the objective functions so that the trade-offs between performance and cost support decision-making for railway network maintenance. The results of the proposed methodology show that the infrastructure manager can localize field inspections and maintenance efforts on the area with the most critical welds. To showcase the capability of the proposed methodology, measurements from a regional railway network in Transylvania, Romania are employed.
KW - Acceleration
KW - Acceleration measurements
KW - Axles
KW - Degradation
KW - Evolutionary multi-objective optimization
KW - Maintenance
KW - Maintenance engineering
KW - Rail transportation
KW - Rails
KW - Railway infrastructure
KW - Welding
UR - http://www.scopus.com/inward/record.url?scp=85048620530&partnerID=8YFLogxK
UR - http://resolver.tudelft.nl/uuid:bc9f6b58-98f8-4eeb-ac89-49c3ba70e2a1
U2 - 10.1109/TII.2018.2847736
DO - 10.1109/TII.2018.2847736
M3 - Article
AN - SCOPUS:85048620530
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
SN - 1551-3203
ER -