Event Detection Platform (using pattern matching analytics)
事件检测平台(使用模式匹配分析)
基本信息
- 批准号:720636
- 负责人:
- 金额:$ 18.4万
- 依托单位:
- 依托单位国家:英国
- 项目类别:GRD Development of Prototype
- 财政年份:2015
- 资助国家:英国
- 起止时间:2015 至 无数据
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
这个为期18个月的项目旨在建立一个由IT平台组成的预商业演示器,该平台能够部署Cybula的事件检测方法,以监控复杂资产(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;软件分析非常受欢迎,因为许多组织都被监控资产数据流淹没,并且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:(i)资产是否按预期运行?(ii)如果有异常,过去是否发生过该事件?(iii)从以前的事件中可以得出什么结论?最后,(iv)在需要采取行动之前,我们还有多少时间?Cybula的Signal Data Explorer是一个台式软件包,可供专家在建立异常模型或事件特征之前浏览大量多变量数据,然后在存档数据上验证它们。该测试环境吸引了多个市场应用(航空航天、铁路、能源发电、石油和天然气生产以及重症监护医学)的多家“蓝筹”公司的参与,Cybula将继续投资于新功能。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,并部署在持续流式传输的资产数据上。用户将能够构建他们的资产数据并附加输入参数以馈送不同的模型。可视化模块将使用户能够配置屏幕显示以随着时间的推移可视化检测到的异常或事件。该项目预计该演示平台能够处理涉及数千个监控变量和多达100个检测模型同时运行的大规模资产监控问题。
项目成果
期刊论文数量(0)
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会议论文数量(0)
专利数量(0)
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其他文献
Internet-administered, low-intensity cognitive behavioral therapy for parents of children treated for cancer: A feasibility trial (ENGAGE).
针对癌症儿童父母的互联网管理、低强度认知行为疗法:可行性试验 (ENGAGE)。
- DOI:
10.1002/cam4.5377 - 发表时间:
2023-03 - 期刊:
- 影响因子:4
- 作者:
- 通讯作者:
Differences in child and adolescent exposure to unhealthy food and beverage advertising on television in a self-regulatory environment.
在自我监管的环境中,儿童和青少年在电视上接触不健康食品和饮料广告的情况存在差异。
- DOI:
10.1186/s12889-023-15027-w - 发表时间:
2023-03-23 - 期刊:
- 影响因子:4.5
- 作者:
- 通讯作者:
The association between rheumatoid arthritis and reduced estimated cardiorespiratory fitness is mediated by physical symptoms and negative emotions: a cross-sectional study.
类风湿性关节炎与估计心肺健康降低之间的关联是由身体症状和负面情绪介导的:一项横断面研究。
- DOI:
10.1007/s10067-023-06584-x - 发表时间:
2023-07 - 期刊:
- 影响因子:3.4
- 作者:
- 通讯作者:
ElasticBLAST: accelerating sequence search via cloud computing.
ElasticBLAST:通过云计算加速序列搜索。
- DOI:
10.1186/s12859-023-05245-9 - 发表时间:
2023-03-26 - 期刊:
- 影响因子:3
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- 通讯作者:
Amplified EQCM-D detection of extracellular vesicles using 2D gold nanostructured arrays fabricated by block copolymer self-assembly.
使用通过嵌段共聚物自组装制造的 2D 金纳米结构阵列放大 EQCM-D 检测细胞外囊泡。
- DOI:
10.1039/d2nh00424k - 发表时间:
2023-03-27 - 期刊:
- 影响因子:9.7
- 作者:
- 通讯作者:
的其他文献
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