Transportation Engineering and Logistics Management
Transportation Engineering and Logistics Management. 2025; 1: (1) ; 10.12208/j.telm.20250002 .
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CRRC Academy, Beijing
*通讯作者： Xiaoyu Shen,单位：CRRC Academy, Beijing；
本文重点探讨了智能技术的快速发展与工业现场多样化智能需求之间的矛盾。由于智能技术的快速发展，开发难度高，在工业环境中实施智能技术需要相当长的时间。同时，轨道交通设备故障预测与健康管理（PHM）系统种类繁多，以满足不同的需求。这些系统功能独立，接口复杂，容易导致“孤岛”现象。PHM系统的开发成本高，且难以在不同场景下进行修改或复用。在轨道交通智能感知领域，迫切需要一种统一的状态监测技术架构。该架构应能够构建一个结构标准化、接口开放的平台。为了解决这些现有问题，本文提出了一种基于模块化的轨道交通设备智能感知架构。在此架构下，实现了设备智能感知流程编排与测试系统。该系统可用于PHM系统的预处理，从而高效地建立故障感知流程并进行在线测试。这有助于解决将故障诊断技术融入数据处理管理和特定场景根本原因的挑战。
This paper focuses on the contradiction between the rapid evolution of intelligent technology and the diverse intelligence requirements of industrial sites. Due to the rapid evolution of intelligent technology, which is challenging to develop, it will take a considerable amount of time to implement in the industrial setting. Meanwhile, there are various types of fault prediction and health management (PHM) systems for rail transit equipment to meet different requirements. These systems have complex interfaces within independent functional systems, which can result in the Islanding phenomenon. The cost of developing the PHM system is high, and it is difficult to modify or reuse it from one scenario to another. In the field of intelligent perception in rail transit, there is an urgent need for a unified architecture of condition monitoring technology. This architecture should be able to create a platform with standardized structure and open interfaces. In order to address these existing issues, an Intelligent Perception Architecture for Rail Transit Equipment based on modularization is proposed. Within this framework, the Device Intelligent Sensing Process Arrangement and Testing System has been implemented. The system can be used for pre-processing in PHM systems to efficiently establish the fault sensing process and conduct online testing. This helps address the challenge of integrating fault diagnosis technology into data processing management and scene-specific root causes.
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