Data-driven urban rail vehicle critical parts maintenance system based on cloud-edge collaboration

Abstract

In view of the limitations of traditional maintenance methods for rail vehicles, this study proposes a data-driven maintenance system for critical parts of rail vehicles based on a cloud-side collaborative framework. Currently, there are several major issues with the maintenance of critical parts of rail vehicles, including long maintenance cycles, difficult troubleshooting, high maintenance costs, low maintenance efficiency, irregularities in data management, and a low level of informatization. To address these problems, a cloud-edge collaboration approach is adopted. The maintenance information of the vehicles is synchronized and exchanged in real-time with the cloud data center. Sensor and Internet of Things (IoT) technologies are used to collect real-time operational and status data of rail vehicles. The data is then analyzed and modelled in the cloud data center. The system achieves fault prediction and remaining useful life prediction for key components of the rail vehicles by applying machine learning algorithms. The experimental results demonstrate that compared to traditional maintenance methods, the system enables more effective troubleshooting and remaining useful life prediction of critical components of the vehicles. It improves maintenance efficiency and safety while also offering practical application value.