{"id":5128,"date":"2019-09-23T10:42:05","date_gmt":"2019-09-23T10:42:05","guid":{"rendered":"https:\/\/www.uptime-h2020.eu\/?page_id=5128"},"modified":"2021-02-12T14:44:27","modified_gmt":"2021-02-12T14:44:27","slug":"uptime_sense","status":"publish","type":"page","link":"https:\/\/www.uptime-h2020.eu\/index.php\/uptime-platform\/uptime_sense\/","title":{"rendered":"UPTIME_SENSE"},"content":{"rendered":"\t\t
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UPTIME_SENSE <\/strong>
Data Acquisition and Manipulation 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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UPTIME_SENSE serves as a modular data acquisition and manipulation component of the UPTIME Platform<\/a><\/strong>. 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.<\/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_SENSE ARCHITECTURE<\/strong><\/h4>\n

The UPTIME_SENSE is 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. It also brings configurable diagnosis capabilities on the edge, e.g. for real-time or off-the-grid applications.<\/p>\n

The _SENSE component architecture consists of _SENSEcore and _SENSEnode. The _SENSEcore is one unit per monitored asset and consolidates all data from this asset. It allows for performing edge data analysis and state detection and forwards the information set to the UPTIME_CLOUD. The _SENSEnode(s) are a set of 1 to n units per asset monitoring the individual sections and parameters of the asset.  Each node captures and pre-processes specific sensor data from the asset and forwards this dataset to the _SENSEcore unit.<\/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

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<\/span><\/strong>KEY ADVANTAGES OF UPTIME_SENSE:<\/span><\/strong>\n<\/h4>

 \u2022  Sensor and data consumer-agnostic hardware abstraction      gateway<\/p>\n

\u2022  Adaptive, flexible data collection and diagnosis tool chain<\/p>\n

\u2022  Efficient stream data processing on the edge with cloud functionality<\/p>\n

\u2022  Supports both fixed and mobile implementation<\/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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IMPLEMENTATION OF UPTIME_SENSE
<\/strong><\/h4>

The _SENSEcore<\/em> and _SENSEnodes<\/em> used for the FFT Business Case are being developed by BIBA together with FFT. The main focus has been on establishing a hardware basis for the sensor data acquisition from the individual assets. The _SENSEnode<\/em> acquires data from the motion sensors (i.e. accelerometer, gyroscope, magnetometer), environment sensors (i.e. barometric pressure, humidity, light intensity, air temperature) at fixed sampling rate.<\/p>

It is then processed on the sensor controller of the _SENSEnode<\/em> to derive measurable physical quantities.Once data acquisition from the _SENSEnodes<\/em> is complete, the forwarding of the data to the UPTIME_CLOUD<\/em> is facilitated by the _SENSEcore<\/em>. Afterwards, the sensor data along with device identification, sensor type and the measured physical quantity are batch uploaded to the UPTIME_CLOUD<\/em>. Please feel free to contact us for more details about the deplyoment of the UPTIME_SENSE in your context.<\/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 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_DETECT aims to identify the topical state\/condition of technical equipment by continuously observing sensor data streams. UPTIME_PREDICT includes abnormal behaviour of technical equipment and accordingly the classification of the condition state (simple example could be traffic light indication such as green, yellow red state). This is done by the possibility to orchestrate so\u2010called calculation flows based on diagnosis and prediction algorithms <\/em>that are already built in the algorithmic framework of the tool or that are built on purpose by implementing a simple programming interface.<\/p>

More <\/a><\/p><\/div><\/div><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t

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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>\n\n

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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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SCIENTIFIC PUBLICATIONS<\/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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