A New Hybrid Approach for Identifying Obsolescence Features: Applied to Railway Signaling Infrastructure

Elie Saad (1, 2, 3) , Mariem Besbes (1) , Marc Zolghadri (1) , Victor Czmil (3) , Vincent Bourgeois (3)

1 ISAE-Supméca - ISAE-Supméca Institut Supérieur de Mécanique de Paris
2 CY - CY Cergy Paris Université
3 SNCF Réseau [La Plaine st Denis]

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.

Keywords: component obsolescence; obsolescence management; feature selection; feature extraction; machine learning; hybrid approach

Domains: Signal and Image Processing

Elie Saad, Mariem Besbes, Marc Zolghadri, Victor Czmil, Vincent Bourgeois. A New Hybrid Approach for Identifying Obsolescence Features: Applied to Railway Signaling Infrastructure. 2024. ⟨hal-04456703⟩