无线预测性维护以修复过时的走动计划
技术
- 分析与建模 - 预测分析
- 功能应用 - 企业资产管理系统 (EAM)
- 网络与连接 - WiFi
- 传感器 - 压力传感器
- 传感器 - 温度传感器
- 传感器 - 振动传感器
适用行业
- 生命科学
适用功能
- 维护
用例
- 预测性维护
挑战
直到去年，C&W Services 一直在其领先的生命科学客户之一使用手动状态监测程序。最好的情况是每 30 天手动收集一次数据，即使在最关键的机器上也使用手持数据记录器。数据收集后，所有的数据分析都必须外包给第三方进行分析。这种方法有几个限制：
1. 计划外停机
2. 人力短缺
3. 安全和使用机器
4. 手动过程收集的读数不一致
客户
C&W 服务
关于客户
解决方案
在评估了几个选项后，C&W Services 部署了 Petasense 基于 IIoT 的 PdM 技术来监控、分析和预测重要工业设备的健康状况，例如 AHU、泵、压缩机、排气扇和冷却器。 C&W Services 安装了名为 Motes 的三轴振动传感器，将它们连接到设施的 Wi-Fi 网络，并立即开始接收数据和可操作的情报。
“来自 Motes 的三轴数据通过增加触手可及的可用数据的数量和种类来提高诊断能力，”Auton 说。 “此外，当你在多个维度和机器上同时读取读数时，它们会变得更有价值。” “此外，它非常易于安装。” Petasense 技术的直观、简单和优雅给 Auton 留下了深刻的印象。对于 C&W 服务团队来说，易于实施及其在 LAN（IP 802.3 协议）上运行的能力是一个巨大的价值驱动因素。
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