{"id":5138,"date":"2019-09-23T10:42:34","date_gmt":"2019-09-23T10:42:34","guid":{"rendered":"https:\/\/www.uptime-h2020.eu\/?page_id=5138"},"modified":"2021-02-17T11:34:01","modified_gmt":"2021-02-17T11:34:01","slug":"uptime_detect_predict","status":"publish","type":"page","link":"https:\/\/www.uptime-h2020.eu\/index.php\/uptime-platform\/uptime_detect_predict\/","title":{"rendered":"UPTIME_DETECT & _PREDICT"},"content":{"rendered":"\t\t
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UPTIME_DETECT & _PREDICT <\/strong>
Stream Data Analytics Module<\/h2><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Predictive maintenance in general describes strategies and actions to prevent breakdowns of technical equipment by early intervention in wear\u2010out or failure situations.
While detection and diagnosis focus on the assessment of technical conditions at the current time, prediction and prognosis try to look into the future to predict the most likely future technical conditions of equipment. Both play though a crucial role in predictive maintenance.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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UPTIME _DETECT & _PREDICT <\/h2>\n
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\u2022\u00a0\u00a0\u00a0 The UPTIME_DETECT & _PREDICT component of the UPTIME Platform enables regular time\u2010based or event\u2010based observations of data coming from sensors and embedded computing systems that are attached to technical systems.<\/p>

\u2022\u00a0\u00a0 The UPTIME_DETECT & _PREDICT component plays a key role for data scientists in charge of developing, testing and deploying algorithmic calculations on data streams. In this way, the component is able to identify the current condition of technical equipment and to give predictions.<\/p><\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n\n

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KEY ADVANTAGES OF UPTIME_DETECT & _PREDICT :<\/h2>\n
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\u2022 Fast engineering of the data-science & prediction algorithms and applications, in particular with reference to maintenance use-cases<\/p>

\u2022 Easy to integrate with other decision-making tools via an open interface<\/p>

\u2022 Fast deployment with scalability to monitor and use predictions models in larger fleets of similar products<\/p><\/p>\n <\/div>\n\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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In the context of the complete UPTIME Platform<\/a><\/span><\/strong>, the _SENSE component is required in order to process sensed data streams to the _DETECT & _PREDICT component. An advancement of the state of the art is intended by integrating backflows from other UPTIME components, such as _ANALYZE, _FMECA, and _DECIDE.<\/p>

In the FFT Business Case<\/a><\/strong><\/span>, _DETECT is the component that takes measurement (sensor) data as input and detects anomalies according to defined rules. The rules can be pre-defined or configured by a domain expert, but also automatically generated e.g. by using AI. _DETECT then generates events from detected anomalies, which can be passed on to other modules for further processing. _PREDICT takes event information generated by _DETECT and estimates a prediction of a certain failure that is expected to happen based on the input event(s) and a user configured set of rules.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Stream Analytics Process of UPTIME_DETECT & _PREDICT<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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UPTIME Other Modules :<\/strong><\/h2><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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UPTIME_SENSE serves as modular data acquisition and manipulation components of the UPTIME Platform. The SENSE component captures data from a high variety of sources and cloud environments. It can connect to both analogue and digital data sources via numerous protocols, acquire data from these heterogeneous data sources, and integrate them towards a configurable data set. It is also capable of storing and intelligently handling and filtering the data acquired and can provide it to other subsequent UPTIME components in the form of sensor data streams for further analysis and processing. Moreover, it brings configurable diagnosis capabilities on the Edge, e.g. for real-time or off-the-grid applications.<\/p>

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UPTIME_ANALYZE is a data analytics engine driven by the need to leverage manufacturers\u2019 legacy data and operational data related to maintenance, and to extract and correlate relevant knowledge. The ANALYZE component is designed to handle data\u2010at\u2010rest which signify data collected from various sources and physically stored across different manufacturers\u2019 information systems.<\/p>

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UPTIME FMECA, Failure Modes Effects and Criticality Analysis, aims to assess failure impacts of a system components. The FMECA component starts from the identification of the failure modes (i.e. how something can break down or fail) associated to each system’s component of an equipment and analyse the impact of such failures on the whole system according to its physical and logical design.<\/p>

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On the basis of (near) real-time predictions about future failures that lay outside the \u201cnormal states space\u201d, DECIDE is enacted online in order to generate proactive action recommendations, i.e. recommendations about optimal (perfect or imperfect) maintenance actions and the optimal times of proactive action implementation. To do this, it estimates when the Expected Maintenance Loss will be minimized.<\/p>

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UPTIME_VISUALIZE provides configurable visualization to save time analysing data and getting insights, to support decision making and develop new solutions.<\/p>

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UPTIME_DETECT & _PREDICT Stream Data Analytics Module https:\/\/www.youtube.com\/watch?v=rkpwp8rh6sU&feature=emb_logo Predictive maintenance in general describes strategies and actions to prevent breakdowns of technical equipment by early intervention in wear\u2010out or failure situations.While detection and diagnosis focus on the assessment of technical conditions at the current time, prediction and prognosis try to look into the future to predict the most likely future technical […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":4568,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-5138","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/pages\/5138","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/comments?post=5138"}],"version-history":[{"count":43,"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/pages\/5138\/revisions"}],"predecessor-version":[{"id":6851,"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/pages\/5138\/revisions\/6851"}],"up":[{"embeddable":true,"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/pages\/4568"}],"wp:attachment":[{"href":"https:\/\/www.uptime-h2020.eu\/index.php\/wp-json\/wp\/v2\/media?parent=5138"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}