Automatic alarm setup using extreme value theory
Automatic alarm setup using extreme value theory
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DOI：
10.1016/j.ymssp.2019.106417
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发表时间：
2020-05
影响因子：
8.4
通讯作者：
D. Toshkova;Matthew F. Asher;P. Hutchinson;N. Lieven
中科院分区：
文献类型：
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作者：
D. Toshkova;Matthew F. Asher;P. Hutchinson;N. Lieven
在本文中，我们概述并展示了一个更强大的解决方案，以监测旋转机械的健康状态的应用。为了避免触发假警报，使用无监督学习系统识别各个操作条件，并为每个条件指标设置阈值水平。此外，我们亦会利用数据质量评估技术，筛选数据中的异常值，并将广义极值理论应用于每种操作条件的既定基线，以确定警告和警报阈值。GEV理论不做prioranya假设的分布时，基线统计数据建立不同于其他统计技术推导极限thresholds.The适用性和所提出的方法的好处证明了使用三个数据集包含过程参数和振动测量从工业发电厂。结果表明，与仅使用统一的阈值水平相比，确定警报和警报阈值以及相关的操作条件提供了对机器部件健康状态的更鲁棒的指示。
In the present paper we outline and demonstrate the application of a more robust solution to monitoring the health state of rotating machinery. In order to avoid triggering false alarms, the individual operational conditions are identified using unsupervised learning system and the threshold levels are set for each condition indicator. The data are also screened for anomalous outliers by using data quality assessment techniques.Warning and alarm thresholds are determined by applying Generalised Extreme Value (GEV) theory to an established baseline for each operational condition. GEV theory does not make anya prioriassumption about the distribution when the baseline statistics are established unlike other statistical techniques for deriving limit thresholds.The applicability and the benefits of the proposed approach are demonstrated using three data sets containing both process parameters and vibration measurements from industrial power generation plants. The results show that determining alert and alarm thresholds alongside the relevant operational conditions provides a more robust indication of the machine components health state compared to using only uniform threshold levels.