下载PDF
Making Magic Real: IIoT Analytics Ensure Every Guest Experiences Disney’s Magic
技术
- 分析与建模 - 数字孪生/模拟
- 平台即服务 (PaaS) - 应用开发平台
适用功能
- 维护
- 产品研发
用例
- 数字孪生
- 预测性维护
服务
- 系统集成
挑战
迪士尼乐园及度假村以其有趣、充满活力和最先进的体验而闻名。然而，提供这些无缝运营是一项重大挑战，特别是考虑到迪士尼景点的复杂性。例如，《星球大战：抵抗组织崛起》游乐设施系统需要实时分析和管理工业物联网 (IIoT) 传感器生成的 30,000 到 40,000 个数据点。迪士尼非常重视确保游客在场时景点的可用性和运营能力。这意味着必须精确地计划和执行维护，以保持游乐系统完美运行。挑战在于将生成的大量数据转化为可操作的见解以进行预测性维护。
关于客户
迪士尼华特迪士尼世界度假区和迪士尼乐园度假区拥有一些世界上最先进的主题公园景点。它们将令人难以置信的设计和最先进的技术相结合，每天将成千上万的客人带入一个想象的世界。迪士尼致力于每次为每位客人提供无缝且难忘的体验。为了实现这一目标，迪士尼非常重视确保游客在场时景点的可用性和运营能力。这涉及精确规划和执行维护，以保持游乐系统完美运行。
解决方案
Disney 使用 Hitachi Vantara Pentaho 平台的解决方案构建了强大的数据管理和分析平台。该平台从遍布景点的 IIoT 传感器捕获、提取和分析数百万个数据点。它还使用人工智能来评估资产状况并预测何时应该安排维护，以保持每个组件处于完美的运行状态。物理数字孪生技术丰富了从工业物联网传感器接收的数据。使用游乐设施复杂的机械和电气系统的数学模型将数据输入详细的模拟中。该模拟自动得出速度、加速度和扭矩等测量值，帮助迪士尼实时了解每个游乐系统组件的状况。该技术首次在米奇和米妮的逃亡铁路上得到展示，改善了对游乐设施机械操作的实时监控和分析。
运营影响
数量效益
相关案例.
Case Study
Remote Monitoring & Predictive Maintenance App for a Solar Energy System
The maintenance & tracking of various modules was an overhead for the customer due to the huge labor costs involved. Being an advanced solar solutions provider, they wanted to ensure early detection of issues and provide the best-in-class customer experience. Hence they wanted to automate the whole process.
Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
Case Study
Aircraft Predictive Maintenance and Workflow Optimization
First, aircraft manufacturer have trouble monitoring the health of aircraft systems with health prognostics and deliver predictive maintenance insights. Second, aircraft manufacturer wants a solution that can provide an in-context advisory and align job assignments to match technician experience and expertise.
Case Study
Integral Plant Maintenance
Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).
Case Study
Asset Management and Predictive Maintenance
The customer prides itself on excellent engineering and customer centric philosophy, allowing its customer’s minds to be at ease and not worry about machine failure. They can easily deliver the excellent maintenance services to their customers, but there are some processes that can be automated to deliver less downtime for the customer and more efficient maintenance schedules.
