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Increasing Asset Health & Uptime: Chiller Connect
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
- 分析与建模 - 实时分析
- 功能应用 - 企业资产管理系统 (EAM)
- 网络与连接 - 洛拉
- 传感器 - 温度传感器
适用行业
- 设备与机械
适用功能
- 维护
用例
- 预测性维护
挑战
在确保其冷却设备以最高效率运行时，公司面临着前所未有的挑战。挑战之一是需要了解设备的性能以优化其使用、健康并预测问题和故障。让 HVAC 服务工程师持续监控冷水机组是不现实的。
客户
未公开
关于客户
将基于嵌入式的冷水机组数据和人工智能工程师相结合，可以从维护转向预防和可预测性。优化资产健康、最大限度地减少资产故障和了解最佳维护计划对于减少运营至关重要
解决方案
智能传感器与基于嵌入式的冷水机数据基础设施相结合，可提供对冷水机运行状况和维护需求的实时、历史和预测性洞察。
收集的数据
Asset Status Tracking, Asset Utilization, Cooling Meters, HVAC ( heating, ventilation, air conditioning), Uptime
运营影响
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