C3 IoT > 实例探究 > 用于预测 HVAC 冷水机组故障的企业 AI

用于预测 HVAC 冷水机组故障的企业 AI

技术

分析与建模 - 机器学习
平台即服务 (PaaS) - 应用开发平台

适用行业

教育
设备与机械

适用功能

维护

用例

楼宇自动化与控制
预测性维护

服务

数据科学服务
系统集成

客户

财富 500 强制造业

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关于客户

财富 500 强制造公司，业务遍及全球。该业务在收购和剥离方面都很活跃，这使得 IT 基础架构非常活跃。

全球设备制造商和服务公司
100,000 名员工
300亿美元的收入

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挑战

一家财富 500 强制造商的建筑系统部门提供设备和服务，为客户提供舒适感，同时优化建筑能源支出。从历史上看，楼宇系统被动地进行冷水机组维护，导致业务中断和停机以及昂贵的紧急维修，最终影响客户满意度。

该制造商需要一个可靠的解决方案，该解决方案能够快速集成所有相关设备和设施数据源，并使其能够减少其商用加热、通风和冷却 (HVAC) 冷水机组系统的停机时间和成本高昂的计划外维护。

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解决方案

该制造商为其 165 台冷水机部署了 C3 AI Reliability 以实现这些目标。客户之所以选择 C3 AI，是因为它具有快速集成传感器数据、标准化和集群不同读数以及运行机器学习算法以在故障发生之前识别恶化状况的成熟能力。

在 4 天内，C3 AI 和客户为所有 165 台冷水机组加载、标准化和映射了 3 年的传感器数据，对这些数据创建了自定义分析，并配置了机器学习算法来预测冷水机组故障事件。 C3 AI 可靠性超过了确定的准确度和精度目标。

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运营影响

The deployment of C3 AI Reliability led to significant operational improvements. The manufacturer was able to rapidly integrate sensor data, normalize and cluster disparate readings, and run machine learning algorithms to identify deteriorating conditions before failures occur. This proactive approach to maintenance reduced business disruptions and downtimes, leading to improved customer satisfaction. The project was completed in less than a week, demonstrating the speed of development and deployment. The machine learning model achieved a precision of 73% and a recall of 71%, exceeding the identified accuracy and precision targets.

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数量效益

$178M annual benefit identified
Project completed in 4 weeks from receiving data to model delivery
Achieved 73% model precision

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