Research on Data Coupling and Independent Consistency for Urban Rail Transit Intelligent Operation-maintenance

Abstract

Objective In the context of intelligent operation-maintenance, existing algorithm exhibits low accuracy, resulting in high false alarm rates, hence it is necessary to conduct analysis of data coupling and independent consistency in train operation data. Method The definition of coupling and independent consistency are established from both statistical and data-driven perspectives. Train operation states are divided into four stages based on the absolute value change rate of acceleration: stationary, stable operation, start-up acceleration, and brake deceleration. Corresponding data slices are generated, and comprehensive quantile plots and correlation coefficients are utilized to analyze the cumulative mainline operation data of traction and braking systems, quantifying the coupling relationship between systems. Linear regression, support vector machine, LightGBM, and K nearest neighbors models are constructed to decouple the data, rendering the traction and braking system data normal, with related variables conforming to independence and consistency, so as to meet the prerequisites of a joint conditional probability distribution. Result & Conclusion The research findings indicate that data decoupling enhances the independent consistency of raw data between systems. From an engineering perspective, the LightGBM model exhibits optimal performance in both real time and offline states, achieving no less than 50% optimization rates across all quantitative analyses. By utilizing decoupled data, it becomes feasible to issue early warnings for potential faults even in cases of limited or missing fault samples, effectively reducing false alarm rates in intelligent operation-maintenance while enhancing fault prediction accuracy.

Keywords

• rail transit
• intelligent operation-maintenance
• fault early-warning
• support vector machine
• lightgbm model
• k-nearest neighbors model