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Large-scale Implementation of Wireless Predictive Maintenance
技术
- 分析与建模 - 预测分析
- 功能应用 - 企业资产管理系统 (EAM)
- 传感器 - 压力传感器
- 传感器 - 温度传感器
- 传感器 - 电压传感器
适用行业
- 公用事业
适用功能
- 维护
用例
- 预测性维护
挑战
2016 年，Arizona Public Service (APS) 决定进入加州 ISO (CAISO) 市场，这使得他们可以向加州市场出售电力。他们的关键资产之一是位于凤凰城外 50 英里处的 420 兆瓦无人调峰电厂圣丹斯。进入 CA 能源市场意味着工厂的启动时间增加了两倍，运行时间几乎立即增加了一倍。
他们开始寻找无线预测性维护 (PdM) 系统，因为运行时间通常是没有人在现场，这意味着传统形式的 PdM 是不可能的。通常情况下，专家会收集设备的振动和其他状态数据，但必须在操作过程中获取，并且很难将人员派往现场。
“可靠性是我们最关心的问题，”设备可靠性工程主管 Don Lamontagne 评论道。 “如果我们无法在需要时发电，我们将面临巨大的潜在收入损失以及罚款。”
客户
亚利桑那州公共服务
关于客户
Arizona Public Service 是亚利桑那州最大的电力公司，拥有 6,300 兆瓦的发电量，为超过 11 个县的 100 万客户提供服务。
解决方案
APS 选择了领先的 IIoT 初创公司 Petasense，因为它符合这四个标准以及更多标准。他们喜欢机器学习算法自动分析来自传感器的数据，从而节省以前检查健康机器所花费的时间。鉴于 Sundance 的远程特性，从任何地方访问可靠性数据并允许远程人员与原始数据和文档维护任务进行交互的能力非常重要。 APS 喜欢 Vibration Motes 和 Transmitters，它们可以带来来自多种传感器类型（压力、温度、电流）的数据，从而扩展到不同类型的资产。
中央 APS 团队希望与工厂合作，以帮助摆脱被动维护文化。他们选择了 100 多个旋转资产进行监控，包括关键泵、电机和风扇。在大多数情况下，他们能够在大约 30 分钟内安装 Motes，而无需关闭设备。
运营影响
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