具有预测性维护和安全通信功能的智能列车控制和监控系统

IF 3.9 Q2 TRANSPORTATION
Alfian Akbar Gozali , Muhammad Faris Ruriawan , Andry Alamsyah , Yudha Purwanto , Ade Romadhony , Febry Pandu Wijaya , Fifin Nugroho , Dewi Nala Husna , Agri Kridanto , Anang Fakhrudin , Mu’ammar Itqon , Sri Widiyanesti
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引用次数: 0

摘要

预测性维护是一种主动的、数据驱动的服务维护方法,旨在在潜在问题发生之前识别它们。现代列车有一个复杂的列车控制和监控系统(TCMS),一个车辆处理单元,提供列车状态条件。在专有的中药中药管理系统的基础上,设计了具有两种主要功能的智能化中药中药管理系统。首先,数据分析功能可以预测产品的年龄并提供实时通知。其次,存在具有数据安全保护的移动条件下的强大基础设施。因此,作者将该解决方案命名为Smart TCMS。本研究设计了一个用户友好的仪表盘,以方便实时状态监控和及时通知任何检测到的问题,重点关注不同级别的部件严重问题:空调(低严重程度),电池(中等严重程度)和牵引系统部件(高严重程度)。该解决方案已在一辆电动柴油列车上实施,这是印度尼西亚铁道车辆工业(INKA)的产品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart train control and monitoring system with predictive maintenance and secure communications features
Predictive maintenance is a proactive and data-driven approach to service maintenance that aims to identify potential problems before they occur. Modern trains have a sophisticated train control and monitoring system (TCMS), a vehicle processing unit to deliver train status conditions. On top of the proprietary TCMS system, the authors designed an intelligent TCMS fitted with two main functions. First, the data analytics features predict the product age and deliver real-time notifications. Second, a robust infrastructure for mobile conditions with data security protection exists. Thus, the authors named the solution as Smart TCMS. This research has designed a user-friendly dashboard to facilitate real-time condition monitoring and timely notification of any detected problems, focusing on different level component severity problems: air conditioning (low severity), battery (medium severity), and traction system components (high severity). This solution has been implemented on an electric diesel train, an Indonesian Rolling Stock Industry (INKA) product.
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来源期刊
Transportation Research Interdisciplinary Perspectives
Transportation Research Interdisciplinary Perspectives Engineering-Automotive Engineering
CiteScore
12.90
自引率
0.00%
发文量
185
审稿时长
22 weeks
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