Research on Key Technologies of Intelligent Maintenance for Rail Transit in Henan

Abstract

Background: With the rapid expansion of Henan's rail transit network, the traditional manual operation and maintenance mode can no longer meet the requirements for high efficiency, safety, and economy, and there is an urgent need to transition toward intelligent and predictive maintenance. Objective: This review explored the development status of intelligent maintenance for subway tunnels, tracks and vehicles under the background of multi-network integration in Henan Province, analyze the key dilemmas including inconsistent standards, data silos, insufficient algorithm robustness and inadequate system integration, solve the practical problems such as low operation efficiency and potential safety risks, clarify the obstacles restricting the upgrading of intelligent maintenance, and provide theoretical support and practical guidance for constructing a full-lifecycle intelligent operation and maintenance system. Method: Domestic and foreign literatures and typical cases from 2020 to 2025 were systematically reviewed, and the application effects of core intelligent technologies in Henan rail transit were comprehensively compared and analyzed. Results: High-precision intelligent detection and fault diagnosis systems have been formed for tunnel, track and vehicle components, which effectively support the transformation from regular maintenance to predictive maintenance. Henan metro intelligent maintenance has achieved breakthroughs in individual technologies, but the current system still faces four core bottlenecks: non‑uniform multi‑system standards, severe data silos, insufficient algorithm robustness, and lack of system integration. Specifically, cities such as Zhengzhou and Luoyang have not yet established a cross‑system condition perception and information exchange mechanism among high‑speed rail, intercity railways, suburban railways, and subways, making it difficult to interconnect inspection data, maintenance records, and asset ledgers. Existing intelligent detection algorithms perform well in laboratory environments, but their robustness and generalization capabilities under complex on‑site conditions such as lighting variations, vibrations, and electromagnetic interference still require systematic validation. Moreover, a balance has yet to be struck among the high cost, lightweight design, and low‑power consumption requirements of intelligent maintenance equipment. Conclusion: Current pilot projects are largely characterized by "isolated demonstrations and fragmented construction", lacking a closed‑loop integration architecture from the perception layer and data layer to the decision‑making layer and execution layer. Therefore, there is an urgent need to build a "technology‑management integrated" full‑lifecycle intelligent operation and maintenance ecosystem. Future efforts should focus on breaking through key technologies such as multi‑source heterogeneous data fusion, cross‑system collaborative diagnosis, and dynamic maintenance decision support, to create a full‑chain intelligent maintenance platform covering "perception‑analysis‑decision‑execution", supported by unified standards and evaluation systems, so as to truly achieve the transition from individual technology breakthroughs to systemic ecological capability.