技术
- 分析与建模 - 大数据分析
- 分析与建模 - 预测分析
- 功能应用 - 车队管理系统 (FMS)
- 基础设施即服务 (IaaS) - 其他
适用行业
- 运输
适用功能
- 物流运输
用例
- 车队管理
客户
未公开
关于客户
运输和物流行业的全球性公司
挑战
组织经常使用基于时间和使用的维护计划来为其车队实施维护策略。虽然总体上是有效的,但基于时间和使用情况的时间表并未考虑驾驶模式、环境因素和当前部署在车辆内测量曲柄电压、点火电压和加速度的传感器,所有这些都对整体健康有重大影响的车辆。在典型的车队中,很大比例的道路呼叫与电气故障有关,电池故障是常见原因。电池故障会导致服务协议水平未达到,并需要昂贵的重新调整计划以提供替换车辆。为了减少计划外维护的影响,运输物流公司对 C3 Vehicle Fleet Analytics 的试用感兴趣。
解决方案
一家全球运输物流企业在不到一周的时间内完成了C3 Vehicle Fleet Analytics的试用,展示了在C3物联网平台上快速开发大数据预测分析应用的能力。该试验的范围是分析 10,000 辆具有历史维护记录和 3 年传感器数据的车辆。 C3 IoT 首先定义了数据模型来存储车辆和车辆传感器数据。使用基于元数据的 C3 类型系统,C3 IoT 快速定义了数据和规范对象模型以及将源对象转换为 C3 数据模型所需的转换。这使 C3 IoT 团队能够在一天内快速获取所有 10,000 辆汽车(约 1 TB)的传感器数据。此外,C3 IoT 基于车辆传感器数据定义了 26 个时间序列分析。然后将这些分析输入机器学习分类器以预测电池故障。在不到一周的工作时间内,C3 IoT 团队: • 为车辆操作开发了数据和规范对象模型 • 加载了 1 TB 的车辆和车辆传感器数据 • 开发了 25 个时间序列分析 • 定义并执行了一个机器学习分类器整个数据集来预测哪些车辆会出现电池故障
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