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The Internet of Trains
技术
- 分析与建模 - 大数据分析
- 基础设施即服务 (IaaS) - 其他
适用行业
- 运输
适用功能
- 产品研发
用例
- 根因分析与诊断
挑战
全世界的火车运营商都有望创造奇迹，即永远不会迟到。因此，要满足严格的服务和可用性目标，高效的维护计划非常重要。支持数据的功能对西门子来说是必须的。反应性维护（事故发生后）和例行的预防性维护及其目视检查和定期更换组件已不再足够。我们已经转向更具成本效益、基于状态的预测性维护。通过诊断传感器数据的传输和远程监控来测量组件的实际状况；数据也用于分析模式和趋势。这有助于预测组件何时可能发生故障，因此可以在发生任何不良情况之前对其进行修复。为了确保这种方法的商业可持续性，西门子需要使用和再利用现有数据，创建一种“火车互联网”。为此，他们正在近乎实时地分析传感器数据，这意味着他们可以非常迅速地做出反应，确保客户运输服务不会中断。 “在影响运营之前，仅使用来自火车的数据很难定义每个问题”，克雷斯解释道。然而，最近的成功案例证明一切皆有可能。
客户
西门子
关于客户
西门子
解决方案
在英国，西门子与一家大型欧洲火车运营商在其区域路线之一上开展了试点项目。该项目分析了一个相对较小的数据集，该数据集包含一百万个传感器日志读数，在一年中以五分钟的间隔采集。分析师测量了来自 300 个不同传感器的组件温度和压力等变量。这些数据与数千个相应的故障和修复报告重叠。然后团队结合数据源，定义最相关的引擎问题，并将数据划分为适当的部分。他们使用 Teradata Aster Discovery Platform 卓越的一系列分析工具从不同角度评估组合数据。它突出了有助于预测发动机问题和识别引发其他组件故障的故障元素的变量。西门子随后应用 Aster nPath 函数对不同的传感器进行分类，归类为正常值、高值和低值，然后跟踪变化。这些变化揭示了哪种传感器模式可能导致发动机故障。例如，西门子曾多次发现，当发动机温度从中值降至低值，然后再次升至中值时，三天后发动机出现故障。在验证过程中，将测试数据与总数据集的结果进行比较显示出高度的准确性，证明传感器数据分析不仅可以预测发动机故障，而且可以及时做出反应以防止它们发生。
收集的数据
Condition Based Monitoring, Fault Detection, Maintenance Requirements, Per-Unit Maintenance Costs, Uptime
运营影响
相关案例.
Case Study
Airport SCADA Systems Improve Service Levels
Modern airports are one of the busiest environments on Earth and rely on process automation equipment to ensure service operators achieve their KPIs. Increasingly airport SCADA systems are being used to control all aspects of the operation and associated facilities. This is because unplanned system downtime can cost dearly, both in terms of reduced revenues and the associated loss of customer satisfaction due to inevitable travel inconvenience and disruption.
Case Study
IoT-based Fleet Intelligence Innovation
Speed to market is precious for DRVR, a rapidly growing start-up company. With a business model dependent on reliable mobile data, managers were spending their lives trying to negotiate data roaming deals with mobile network operators in different countries. And, even then, service quality was a constant concern.
Case Study
Digitize Railway with Deutsche Bahn
To reduce maintenance costs and delay-causing failures for Deutsche Bahn. They need manual measurements by a position measurement system based on custom-made MEMS sensor clusters, which allow autonomous and continuous monitoring with wireless data transmission and long battery. They were looking for data pre-processing solution in the sensor and machine learning algorithms in the cloud so as to detect critical wear.
Case Study
Cold Chain Transportation and Refrigerated Fleet Management System
1) Create a digital connected transportation solution to retrofit cold chain trailers with real-time tracking and controls. 2) Prevent multi-million dollar losses due to theft or spoilage. 3) Deliver a digital chain-of-custody solution for door to door load monitoring and security. 4) Provide a trusted multi-fleet solution in a single application with granular data and access controls.
Case Study
Vehicle Fleet Analytics
Organizations frequently implement a maintenance strategy for their fleets of vehicles using a combination of time and usage based maintenance schedules. While effective as a whole, time and usage based schedules do not take into account driving patterns, environmental factors, and sensors currently deployed within the vehicle measuring crank voltage, ignition voltage, and acceleration, all of which have a significant influence on the overall health of the vehicle.In a typical fleet, a large percentage of road calls are related to electrical failure, with battery failure being a common cause. Battery failures result in unmet service agreement levels and costly re-adjustment of scheduled to provide replacement vehicles. To reduce the impact of unplanned maintenance, the transportation logistics company was interested in a trial of C3 Vehicle Fleet Analytics.
Case Study
3M Gains Real-Time Insight with Cloud Solution
The company has a long track record of innovative technology solutions. For example, 3M helps its customers optimize parking operations by automating fee collection and other processes. To improve support for this rapidly expanding segment, 3M needed to automate its own data collection and reporting. The company had recently purchased the assets of parking, tolling, and automatic license plate reader businesses, and required better insight into these acquisitions. Chad Reed, Global Business Manager for 3M Parking Systems, says, “With thousands of installations across the world, we couldn’t keep track of our software and hardware deployments, which made it difficult to understand our market penetration.” 3M wanted a tracking application that sales staff could use to get real-time information about the type and location of 3M products in parking lots and garages. So that it could be used on-site with potential customers, the solution would have to provide access to data anytime, anywhere, and from an array of mobile devices. Jason Fox, Mobile Application Architect at 3M, upped the ante by volunteering to deliver the new app in one weekend. For Fox and his team, these requirements meant turning to the cloud instead of an on-premises datacenter. “My first thought was to go directly to the cloud because we needed to provide access not only to our salespeople, but to resellers who didn’t have access to our internal network,” says Fox. “The cloud just seemed like a logical choice.”
