Heterogeneity-oriented ensemble learning for rail monitoring based on vehicle-body vibration

Abstract

In this study, railway vehicle-body vibration was applied to rail detection for convenient sensor deployment and cost-effectiveness. However, the waveform is difficult to analyze due to damping and interference. Data-driven methods can help concatenate multidimensional signals and complex rail-surface irregularities but are impressionably uncertain. This study proposes a method in which a deep learning framework is coupled with heterogeneous factors at every link in its ensemble strategy. The instantiation, module foundation, and scenario description establish a concrete system for dealing with the dilemma that an insufficient database hinders feature extraction, causing unproductive capacity. The performance is quantitatively discussed by combining different levels of on-site rail-surface conditions, reaching prediction errors of 4.7% and 6.5% and classifier accuracies of 98.4% and 93.7% for irregularities and defect severities, respectively. This work describes a way to extend self-learner applicability in industry and will facilitate new support for railway track management.