Unsupervised discrepancy-based domain adaptation network to detect rail joint condition

Abstract

Damage to maglev rail joints, which connect adjacent rail segments, threatens the safety and comfort of railway systems. Machine learning methods have been used in combination with online monitoring data to assess the health conditions of maglev rail joints. However, most of the existing methods rely on the data collected in controlled scenarios, such as those involving constant train operation speeds. Given the diversity of operational conditions, a model learned from one known case (source domain) cannot be directly applied to the case of interest (target domain). Therefore, this article proposes a domain adaptation (DA) approach to diagnose the health conditions of maglev rail joints in complex operational conditions. The DA is unsupervised because the source and target domains are characterized by labeled and unlabeled samples, respectively. DA is implemented by integrating the sample moments with different orders into the transfer loss of a neural network. By minimizing the transfer loss, the domain shift caused by the difference in the operational conditions can be reduced, and the knowledge of features learned from the neural network is transferred from the source domain to the target domain. The proposed approach is validated over a dataset of time–frequency spectrograms (TFSs) derived from the experimental acceleration data of maglev rail joints in two operation modes: stable passing and braking. The proposed approach can successfully identify the conditions of the maglev rail joints, i.e., bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition, even when the operation mode of the maglev train changes.