  Electrical component obsolescence poses a major issue especially within
systems with large life cycles. Thus, finding the optimal management solution
for each obsolescence case is as crucial as knowing what to consider when faced
with an obsolescence case. In this paper, a novel hybrid approach for
identifying features affecting electrical component obsolescence management is
introduced, which combines features engineering techniques and expert
knowledge. The method then uses machine learning to predict obsolescence
resolution techniques in order to find the optimal resolution. The motivation
behind this research is driven by the imperative need for SNCF RESEAU to
optimally address and mitigate the challenges posed by electrical component
obsolescence in railway infrastructure.
