Data-driven decision support system for degrading assets and its application under the perspective of a railway component

Abstract

Transportation plays a crucial role in facilitating economic and social interactions, but also significantly contributes to global pollution. To address this sustainability challenge, smart use of resources and transport modes is needed. To contribute to more efficient mobility in worldwide transportation, railways should have better maintenance. Within railway vehicles, the wheelset component is essential for the safety of the train, preventing derailment. However, degradation resulting from the wheel-rail contact during operation may compromise the proper functioning of the components, requiring maintenance to be carried out. Nowadays, the accumulation in the last years of information and the technological ease to generate large amounts of data in inspection and maintenance activities in railway companies allow for implementing robust and reliable data-driven approaches to improve the maintenance of the components. With this scenario in the background, this paper proposes a data-driven decision support system for maintenance actions in degrading assets. Predictive degradation models of wear and damage are developed. The uncertainty of degradation data obtained in measurement condition monitoring activities is quantified and used to evaluate the impact on optimal maintenance strategies. Results obtained indicate that optimized maintenance actions can extend the lifespan of the wheelset and rail components and reduce their long-term maintenance costs. Outputs from the decision support system suggest better maintenance practices to be performed in railway companies.