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Cooperation with VR FleetCare for predictive analytics
技术
- 分析与建模 - 边缘分析
- 分析与建模 - 预测分析
- 功能应用 - 远程监控系统
- 基础设施即服务 (IaaS) - 云计算
- 基础设施即服务 (IaaS) - 其他
- 网络与连接 - 网关
适用行业
- 铁路与地铁
适用功能
- 维护
用例
- 预测性维护
挑战
就生命周期成本和交通安全而言，转向架是铁路车队中最重要的组成部分。除了为铁路车队所有者节省大量成本外，数据驱动的维护还将提高机车车辆的安全性和可用性。预测性维护能力将提高列车的可靠性、成本效益和乘客舒适度。如果能够在机车车辆故障导致交通中断之前对其进行预测，那么列车交通将更加可靠地运行。
客户
VR 车队护理
关于客户
VR FLEETCAREA 多品牌铁路设备服务提供商 VR FleetCare 是芬兰铁路运营商 VR 集团的子公司。我们为客户提供优质的铁路交通设备维修、保养和生命周期服务以及技术
解决方案
此次合作基于 VR FleetCare 的铁路车队技术专长和车队维护优化，以及 EKE-Electronics 在列车自动化系统和远程状态监测方面的经验。
目的是开发一个系统来预测维护需求并优化转向架的服务程序。通过传感器和传感器网关从火车上获取数据，并应用云计算和边缘计算的智能组合进行信号分析。该解决方案利用 EKE 的英国子公司 Humaware 开发的自适应异常检测器和预测分析，并集成到 EKE 的基于云的远程监控软件 Smartvision™ 中。
传感器和传感器网关将在今年安装在 VR 集团的机车和电动火车的转向架上。预计明年开发工作将取得更广泛的成果。
运营影响
相关案例.
Case Study
Building Smart IoT-Connected Railways
• Difficult environment. Communications equipment on trains must function properly in harsh conditions, such as environment temperatures ranging from -25°C to +85°C, according to the EU standard EN50155.• Railway regulations. All products in a train must adhere to strict standards, relating to working vibration, power consumption, and lifetime.• Lengthy process. Time to market in the railway industry can take years from concept to mass production, so product design requires a solid long term vision.
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.
