从燃气轮机数据中识别叶片故障
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
- 应用基础设施与中间件 - 数据可视化
适用行业
- 设备与机械
适用功能
- 维护
用例
- 预测性维护
客户
未公开
关于客户
一家燃气轮机原始设备制造商正在采取创新方法，通过利用机器学习来更多地掌控预测性维护过程，从而防止代价高昂的停机。
挑战
2015 年末，一台已部署的燃气轮机出现两排叶片故障，这对压缩机造成了巨大的二次损坏，导致近两个月的停机时间和高达 3000 万美元的维修成本和机会损失。这种失败虽然很少见，但代表了很难捕捉到的典型灾难性事件。尽管现场工厂运营团队一直在监控资产，但这种特定的故障模式以前是未知的且非常细微，现有的警报没有足够的信息让中小企业及时正确诊断。
OEM 决定评估 SparkCognition 的预测分析解决方案 SparkPredict®，其目标如下：
1. 展示根据涡轮机提供的盲数据检测和区分运行和异常在线稳态条件的能力。
2. 提供有关导致潜在异常的关键因素的更多见解。
3. 提供一个与资产中的实时流数据接口的 UI。
解决方案
在一个季度的过程中，SparkCognition 使用来自相关燃气轮机的两年数据，为每种稳态和瞬态运行模式开发、训练和验证了高性能机器学习模型。 SparkPredict 的数据科学团队利用专有的内部工具和自动化模型构建功能构建了这些独特的模型，以快速有效地执行。
使用这些方法，团队首先清理数据以准备建模。他们将最初的 400 多个变量集减少到最能代表数据集的大约 250 个。然后，使用无监督聚类方法，他们确定了涡轮机的所有各种运行状态，并与客户 SME 一起标记每个已知的故障事件集群。最后，使用来自 OEM 的实际故障事件数据对该模型进行了验证。
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