Remaining useful life prediction of railway wheelsets using a composite wear index under sparse monitoring data - Agustinus Winarno; Ahmad Fauzan Karnadi; Herjuno Rizki Priatomo; Slamet Afif Mansuri; Rioko Aji; Sudianto Sudianto; Miming Kuncoro (Politeknik Negeri Lhokseumawe, 2026)
Accurate prediction of the Remaining Useful Life (RUL) of railway wheelsets is important for operational safety and efficient maintenance planning. This study proposes a physics-informed composite wear index integrating wheel diameter and flange wear and validates it against field operational data. The composite index, W=f(D)∘f(L), combines diameter wear and flange wear through a reprofiling coefficient k, representing diameter reduction per unit flange restoration. Using machining records from a 60-wagon freight train operated by PT Kereta Api Indonesia (1,791 monthly records over 3 years), the field-based median k was estimated at 2.75 mm/mm for the train set and 3.00 mm/mm fleet-wide. The selected modelling value of k = 3.2 mm/mm lies near the upper range of field observations and provides a conservative approximation. Applied to the operational dataset, the index successfully tracked coupled wear progression, showing that 62% of wagons had exceeded the midlife threshold (W ≥ 0.50). A deterministic benchmark using 201 observations across five reprofiling cycles was used to compare five machine-learning models under 30–60% monitoring densities. Linear regression achieved the lowest error (R² ≈ 1.000), while gradient boosting showed the most reliable non-linear performance. The results support the proposed composite wear index as a practical basis for wheelset RUL estimation under limited monitoring data.