Work

Diffusion-based unsupervised distributed acoustic sensing for rail condition monitoring and degradation assessment

Year: 2026

Type: article

Abstract: Railway infrastructure deteriorates continuously under repeated loading and environmental effects, while maintenance decisions still rely on periodic inspections that cannot capture degradation evolution. This study proposes a distributed acoustic sensing (DAS) and diffusion-based reconstruction framework to quantify rail condition from long-term vibration data without requiring dense labels. Using a 2 km metro deployment monitored over one year, reconstruction-based indicators are shown to form a continuous degradation curve, distinguishing different stages of rail corrugation development. The results are consistent with vehicle–track dynamic simulations, indicating that the method reflects underlying degradation behavior. A model trained on a short replaced-rail segment generalizes to network-scale monitoring and identifies early-stage defects confirmed by maintenance records. The proposed approach supports continuous condition assessment and enables maintenance decisions to shift from fixed inspection cycles to data-driven, trend-based interventions.

Source: Computer-Aided Civil and Infrastructure Engineering

DOI: 10.1016/j.cacaie.2026.100152

Authors: Chengjia Han, Shuai Qu, Yuguang Fu, Tao Ma, Liwei Dong, Yaowen Yang

Institutions: Nanyang Technological University, Tongji University, Southeast University

Language: English

Topic: Railway Engineering and Dynamics

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $5,230