## 5 Conclusions
Despite the advances mentioned above, the performance of the methodologies developed so far, especially those involving the detection of damage to the railways, still needs to be evaluated in the face of operational disturbances. There is a lack of studies in the literature evaluating the accuracy of these methodologies, for example, in the face of measurement noise, limitations on sensor positioning, variations in vehicle operating conditions, wind interference, temperature variations, and track irregularities, among other factors. In addition, there is a lack of studies demonstrating how these methodologies can perform in scenarios where there are multiple defects involved, both on the vehicle side and on the track side. Finally, it is crucial to address the gap in research on the application of drive-by techniques in real operational scenarios. Aspects such as storage, data transmission, energy supply, onboard data processing capacity, among other aspects, need to be analyzed in more depth. Bearing in mind the main difficulties referred to above, it is expected that in future work researchers will focus mainly on issues related to the practical applicability of drive-by monitoring through experimental validation of the methodologies proposed so far. These studies should focus on new techniques capable of isolating the influence of environmental/operational factors on damage indicators. In addition, factors such as optimal positioning and the most suitable sensors to ensure better accuracy of the methodologies will inevitably have to be investigated in detail. Concerning data processing and interpretation, further developments should bring more advanced techniques based on artificial intelligence, following the line of many recent works presented. These methodologies have proven to be increasingly powerful in extracting patterns from datasets and have great potential to increase the robustness of damage identification techniques. Finally, based on these developments, there will also be a broad field of research in the design of dedicated monitoring, processing, storage, and data transmission systems to be installed in railway vehicles, compatible with the railway's operational reality. Conceptualization, E.F.S., C.B. and D.R. ; writing--original draft preparation, E.F.S., C.B. and A.M.; writing--review and editing, D.R., A.M. and H.C.; visualization, E.F.S. and C.B. ; supervision, T.N.B. and D.R. ; project administration, T.N.B. All authors have read and agreed to the published version of the manuscript. The authors would like to acknowledge CNPq (Brazilian Ministry of Science and Technology Agency), CAPES (Higher Education Improvement Agency), FAPESP (Sao Paulo Research Foundation) for financial support under grant #2022/13045-1, VALE Catedra Under Rail, project \"FERROVIA 4.0\", with reference POCI-01-0247-FEDER- 046111, cofounded by the European Regional Development Fund (ERDF), through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020) and the Lisbon Regional Operational Programme (LISBOA 2020), under the PORTUGAL 2020 Partnership Agreement\", and project \"SMART WAGONS--DEVELOPMENT OF PRODUCTION CAPACITY IN FOR-TUGAL OF SMART WAGONS FOR FREIGHT\", with reference C644940527-00000048, from the Incentive System to Mobilizing Agendas for Business Innovation, funded by the Recovery and Resilience Plan and by European Funds NextGeneration EU. Not applicable. Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.