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Driving Predictive Maintenance into ThyssenKrupp Elevators
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
- 基础设施即服务 (IaaS) - 其他
- 平台即服务 (PaaS) - 数据管理平台
适用行业
- 设备与机械
适用功能
- 维护
用例
- 预测性维护
挑战
TKE 在全球范围内开展了多项举措，以实现对其电梯的远程监控。然而，没有一个解决方案提供了从传统的被动维护方法转变为预测性甚至先发制人方法所需的数据和洞察力，并且所有解决方案都受到限制其价值的信息过载问题的挑战。 TKE 想要一种解决方案，使其能够预测并快速解决其在全球服务的 120 万部电梯中的大部分的维护问题。
客户
蒂森克虏伯电梯 (TKE)
关于客户
蒂森克虏伯电梯 (TKE) Americas 是美洲最大的电梯生产商，在全球提供和维护超过一百万台电梯。
解决方案
CGI 的开发团队使用 Microsoft 的基于 Azure 的智能系统服务 (ISS)、Microsoft 的 Azure Macing Learning、HDInsight 和我们自己的智能企业框架 (IEF) 构建了一个试点、基于云的电梯监控系统，这有助于快速部署 IoT 应用程序。该系统与 TKE 的电梯传感器集成，利用来自每个传感器的数据，使用 TKE 定义的业务规则处理数据，并使用预测分析生成丰富的数据洞察力。然后，主管和现场技术人员可以通过两种不同的用户界面以维护警报、说明和建议的形式获得由此产生的洞察力。来自用户的反馈集成在系统中，使其随着时间的推移变得更加准确。 CGI 与 Microsoft 合作使用 Microsoft 的机器学习 Azure 服务开发预测数据模型。对来自北美各地电梯的多年历史数据进行分析，生成警报序列映射，以确定故障的根本原因。该系统于 2014 年夏天在华盛顿西雅图地区由 TKE 运营的少量电梯实施。该试点项目取得了成功，使 TKE 能够减少电梯停机时间并改进资源规划、成本预测和维护调度。反过来，TKE 能够为其客户提供更具竞争力的产品。
收集的数据
Downtime, Maintenance Requirements, Maintenance Work Orders, Per-Unit Maintenance Costs
运营影响
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