Anomaly Event Detector
Embodiments are directed to a computer-based tool that can identify an anomalous state of a component in a real-world environment, even if the component experiences gradual and/or seasonal trends. The tool receives data from sensors monitoring a component. The tool uses a trained machine learning mo...
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| creator | You, Jiangsheng Noskov, Mikhail |
| description | Embodiments are directed to a computer-based tool that can identify an anomalous state of a component in a real-world environment, even if the component experiences gradual and/or seasonal trends. The tool receives data from sensors monitoring a component. The tool uses a trained machine learning model to calculate a predicted behavior of the monitored component. Actual behavior of the component, captured by current sensor readings, is compared to the predicted behavior of the component, calculated by the machine learning model, to compute a divergence. The computed divergence is used by a statistical learning method to determine if the component in the real-world environment is in an anomalous state. |
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The computed divergence is used by a statistical learning method to determine if the component in the real-world environment is in an anomalous state.</description><subject>CONTROL OR REGULATING SYSTEMS IN GENERAL</subject><subject>CONTROLLING</subject><subject>FUNCTIONAL ELEMENTS OF SUCH SYSTEMS</subject><subject>MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS</subject><subject>PHYSICS</subject><subject>REGULATING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZBBzzMvPTcypVHAtS80rUXBJLUlNLskv4mFgTUvMKU7lhdLcDMpuriHOHrqpBfnxqcUFicmpeakl8aHBRgZGxsbmZgaGRo6GxsSpAgAOryIy</recordid><startdate>20231123</startdate><enddate>20231123</enddate><creator>You, Jiangsheng</creator><creator>Noskov, Mikhail</creator><scope>EVB</scope></search><sort><creationdate>20231123</creationdate><title>Anomaly Event Detector</title><author>You, Jiangsheng ; Noskov, Mikhail</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2023376012A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2023</creationdate><topic>CONTROL OR REGULATING SYSTEMS IN GENERAL</topic><topic>CONTROLLING</topic><topic>FUNCTIONAL ELEMENTS OF SUCH SYSTEMS</topic><topic>MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS</topic><topic>PHYSICS</topic><topic>REGULATING</topic><toplevel>online_resources</toplevel><creatorcontrib>You, Jiangsheng</creatorcontrib><creatorcontrib>Noskov, Mikhail</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>You, Jiangsheng</au><au>Noskov, Mikhail</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Anomaly Event Detector</title><date>2023-11-23</date><risdate>2023</risdate><abstract>Embodiments are directed to a computer-based tool that can identify an anomalous state of a component in a real-world environment, even if the component experiences gradual and/or seasonal trends. The tool receives data from sensors monitoring a component. The tool uses a trained machine learning model to calculate a predicted behavior of the monitored component. Actual behavior of the component, captured by current sensor readings, is compared to the predicted behavior of the component, calculated by the machine learning model, to compute a divergence. The computed divergence is used by a statistical learning method to determine if the component in the real-world environment is in an anomalous state.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CONTROL OR REGULATING SYSTEMS IN GENERAL CONTROLLING FUNCTIONAL ELEMENTS OF SUCH SYSTEMS MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS PHYSICS REGULATING |
| title | Anomaly Event Detector |
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