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Building Smart IoT-Connected Railways
技术
- 功能应用 - 企业资产管理系统 (EAM)
- 功能应用 - 远程监控系统
- 处理器与边缘智能 - 片上系统
- 传感器 - 温度传感器
- 传感器 - 振动传感器
适用行业
- 铁路与地铁
适用功能
- 商业运营
用例
- 资产健康管理 (AHM)
挑战
• 困难的环境。根据欧盟标准 EN50155，列车上的通信设备必须在恶劣条件下正常工作，例如环境温度范围为 -25°C 至 +85°C。
• 铁路法规。列车上的所有产品都必须遵守与工作振动、功耗和使用寿命相关的严格标准。
• 漫长的过程。铁路行业的上市时间从概念到量产可能需要数年时间，因此产品设计需要坚实的长期愿景。
客户
萨德尔
关于客户
铁路通信提供商
解决方案
• 持久的处理能力。 SADEL 选择英特尔® 凌动™ 处理器为其解决方案提供动力，是因为它们具有低能耗、令人印象深刻的吞吐量，并且能够承受极端的工业温度。
• 可靠的技术。英特尔® 凌动™ SoC 直接焊接到研扬科技计算机板* 底座上的印刷电路上，从而简化了无风扇散热器的实施并提高了可靠性。
收集的数据
Asset Performance, Connectivity Status, Energy Cost Per Unit, Energy Usage, Process Procedure
运营影响
相关案例.
Case Study
Connected Transportation: A Smarter Brain for Your Train with Intel
A modern locomotive, for example, has as many as 200 sensors generating more than a billion data points per second. Vibration sensors surround critical components, video cameras scan the track and cab, while other sensors monitor RPM, power, temperature, the fuel mix, exhaust characteristics, and more.Most of today’s locomotives lack sufficient on-board processing power to make full use of all this data. To make matters worse, the data from different subsystems, such as the brakes, fuel system, and engine, remain separate, stored in isolated “boxes” that prevent unified analysis. The data is available, but the technology needed to process it in the most effective manner is not. As new sensors are added to the machine, the problem escalates.
Case Study
Using LonWorks to Keep Acela Trains Zip Along
Canadian transportation company, Bombardier was tasked with building a bullet train system on rails that were designed for lower speed trains. In addition, they had to ensure safe and optimal operation at high speeds, maximize train uptime and enhance communication with passengers.
Case Study
Delhi NCR Metro: A Mobile App Revolutionizing Public Transportation
The Delhi NCR Metro, a major public transportation system in India, was facing a challenge in providing accurate and comprehensive information to its daily commuters and tourists. The lack of a centralized platform for information about metro station details, train schedules, fare details, parking, elevators, and tourist locations was causing inconvenience to the users. The challenge was to develop a mobile app that could provide all this information accurately and conveniently. The app needed to be equipped with GPS services to help users find the nearest metro and renowned locations. An interactive map was also required to assist travelers who were familiar with the metro lines. The goal was to provide maximum information with minimum input.
Case Study
Automated Railcar Inspections Increase Security and Revenue
Providing industry and government customers with intelligent inspection, automation, safety, and security solutions, Duos Technologies Group, Inc. (“Duos” or the “Company” - Nasdaq: DUOT) continually pushes the boundaries of IT. To keep pace with expanding AI-enabled data capture analytics for its edge railcar inspections, the company chose the latest Dell EMC PowerEdge servers.Duos Technologies’ challenge was finding a way to leverage technology as a force multiplier to meet customer requirements for a better, faster inspection process for trains running at full speed. Duos developed innovative data analytic solutions with AI at the edge to conduct more reliable railcar inspections, which are available 24/7/365 in all climates and conditions.
Case Study
Cooperation with VR FleetCare for predictive analytics
Bogies are the most significant components of the rail fleet in terms of lifecycle costs and traffic safety. In addition to creating significant cost savings for the rail fleet owners, data-driven maintenance will enhance safety and the usability of the rolling stock. The predictive maintenance capability will improve reliability of the trains, cost-efficiency and passenger comfort. Train traffic will operate more reliably when it is possible to predict rolling stock malfunctions before they cause disruptions in traffic.
