A condition diagnosis method for subway track structures employing distributed optical fiber sensing

With the rapid development of urban rail transit, subway track structures have an increasingly serious risk of damage under high-load operations. Traditional detection methods experience several problems, such as limited coverage and a lack of real-time performance, which make it difficult to ensure operation security. Therefore, a new method for diagnosing subway track structure states based on distributed fiber sensing is proposed. First, a method for constructing a correlation model for strain monitoring data based on the optimal space window is proposed to realize the division of measuring points to reduce the computational complexity, and then, the deep generative adversarial network model with residual learning is constructed. Through spatial correlation analysis of the strain of symmetric measuring points, the Mahalanobis distance of the predicted residual is used as the diagnostic factor to realize accurate identification of the orbital structure state. Finally, practical engineering verification shows that the proposed method can effectively eliminate periodic interferences such as temperature and accurately detect local strain anomalies with a positioning error that is less than the measuring point interval (20 cm), providing reliable technical support for the intelligent monitoring and safety of subway track structures.