Event Detection Platform (using pattern matching analytics)

这个为期18个月的项目旨在建立一个由it平台组成的商业前演示器，该平台能够部署Cybula的事件检测方法来监控复杂资产（机器，发动机，基础设施和医疗应用，人员）的行为。这些方法在多变量时间序列数据上使用新颖的模式匹配技术；软件分析是非常需要的，因为许多组织被监控资产数据流所淹没，而他们缺乏与数据交互的工具。这些分析有助于回答的基本问题是：(1)资产是否按预期运行？（ii）如有异常，过去是否发生过？（iii）从以前的事件中可以学到什么；最后（iv）在需要采取行动之前，我们还有什么时间？Cybula的Signal Data Explorer是一款台式软件包，专家可以在构建异常模型或事件签名之前浏览大量多变量数据，然后在存档数据上验证它们。这种测试环境吸引了多个市场应用领域（航空航天、铁路、能源生产、石油和天然气生产以及危重医疗）的几家“蓝筹”公司的参与，cybula继续投资于新功能。该演示器将允许这些模型和事件签名作为工作流从Signal Data Explorer导入，并部署在连续流资产数据上。用户将能够构建他们的资产数据，并附加输入参数来提供不同的模型。可视化模块将使用户能够配置屏幕显示，以便随着时间的推移可视化检测到的异常或事件。该项目预计演示平台能够处理涉及数千个被监测变量和多达100个同时运行的检测模型的大规模资产监测问题。

This 18 month project aims to build a pre-commercial demonstrator consisting of an ITplatform which is capable of deploying Cybula’s event detection methods to monitor thebehaviour of complex assets (machines, engines, infrastructure and with medical applications,people). These methods use novel pattern matching techniques on multi-variate, time-seriesdata; software analytics that are very much in demand as many organisations are swampedwith the flow of monitored asset data and they lack the tools to interact with the data. Thefundamental questions which these analytics help to answer are (i) is the asset operating asexpected? (ii) if there is an abnormality, has this event occurred in the past? (iii) what can belearnt from previous events and finally (iv) what time do we have before action is required?Cybula’s Signal Data Explorer, a benchtop software package, can be used by specialists tobrowse large volumes of multi-variate data before building abnormality models or eventsignatures and then validating them on archived data. This testing environment has attractedthe involvement of several ‘blue-chip’ companies across several market applications(aerospace, rail, energy generation, oil & gas production and critical-care medicine) andCybula continues to invest in new features.This demonstrator will allow these models and event signatures to be imported as workflowsfrom Signal Data Explorer and deployed on continuously streamed asset data. The user willbe able to structure their asset data and attach input parameters to feed the different models.A visualisation module will enable the user to configure screen displays to visualise detectedabnormalities or events over time. The project anticipates the demonstration platform to becapable of handling large scale asset monitoring problems involving thousands of monitoredvariables and up to 100 detection models running simultaneously.