Automatic data-screening framework and preprocessing pipeline to support ML-based prognostic surveillance
The disclosed embodiments relate to a system that automatically selects a prognostic-surveillance technique to analyze a set of time-series signals. During operation, the system receives the set of time-series signals obtained from sensors in a monitored system. Next, the system determines whether t...
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| creator | Wang, Guang C Chotrani, Aakash K Gerdes, Matthew T Wood, Alan P Gross, Kenny C Guo, Beiwen |
| description | The disclosed embodiments relate to a system that automatically selects a prognostic-surveillance technique to analyze a set of time-series signals. During operation, the system receives the set of time-series signals obtained from sensors in a monitored system. Next, the system determines whether the set of time-series signals is univariate or multivariate. When the set of time-series signals is multivariate, the system determines if there exist cross-correlations among signals in the set of time-series signals. If so, the system performs subsequent prognostic-surveillance operations by analyzing the cross-correlations. Otherwise, if the set of time-series signals is univariate, the system performs subsequent prognostic-surveillance operations by analyzing serial correlations for the univariate time-series signal. |
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During operation, the system receives the set of time-series signals obtained from sensors in a monitored system. Next, the system determines whether the set of time-series signals is univariate or multivariate. When the set of time-series signals is multivariate, the system determines if there exist cross-correlations among signals in the set of time-series signals. If so, the system performs subsequent prognostic-surveillance operations by analyzing the cross-correlations. 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Otherwise, if the set of time-series signals is univariate, the system performs subsequent prognostic-surveillance operations by analyzing serial correlations for the univariate time-series signal.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjDEOwjAQBNNQIOAP5gEpAjI9QSAKqIA6OpxNZOH4Tj4Hvo8i8QCqKWY088Lvx8wDZe9MS5lKdQmIPvamSzTgw-llKLZGEiSxg-rkxAuCjzCZjY4inLK5XsonKaaU-8g6LXVMb_gQKDosi1lHQbH6cVGsT8f74VxCuIEKOUTk5nGrKmt31tp6s_2n-QLVm0Hn</recordid><startdate>20230117</startdate><enddate>20230117</enddate><creator>Wang, Guang C</creator><creator>Chotrani, Aakash K</creator><creator>Gerdes, Matthew T</creator><creator>Wood, Alan P</creator><creator>Gross, Kenny C</creator><creator>Guo, Beiwen</creator><scope>EVB</scope></search><sort><creationdate>20230117</creationdate><title>Automatic data-screening framework and preprocessing pipeline to support ML-based prognostic surveillance</title><author>Wang, Guang C ; Chotrani, Aakash K ; Gerdes, Matthew T ; Wood, Alan P ; Gross, Kenny C ; Guo, Beiwen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US11556555B23</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Wang, Guang C</creatorcontrib><creatorcontrib>Chotrani, Aakash K</creatorcontrib><creatorcontrib>Gerdes, Matthew T</creatorcontrib><creatorcontrib>Wood, Alan P</creatorcontrib><creatorcontrib>Gross, Kenny C</creatorcontrib><creatorcontrib>Guo, Beiwen</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Guang C</au><au>Chotrani, Aakash K</au><au>Gerdes, Matthew T</au><au>Wood, Alan P</au><au>Gross, Kenny C</au><au>Guo, Beiwen</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Automatic data-screening framework and preprocessing pipeline to support ML-based prognostic surveillance</title><date>2023-01-17</date><risdate>2023</risdate><abstract>The disclosed embodiments relate to a system that automatically selects a prognostic-surveillance technique to analyze a set of time-series signals. During operation, the system receives the set of time-series signals obtained from sensors in a monitored system. Next, the system determines whether the set of time-series signals is univariate or multivariate. When the set of time-series signals is multivariate, the system determines if there exist cross-correlations among signals in the set of time-series signals. If so, the system performs subsequent prognostic-surveillance operations by analyzing the cross-correlations. Otherwise, if the set of time-series signals is univariate, the system performs subsequent prognostic-surveillance operations by analyzing serial correlations for the univariate time-series signal.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
| title | Automatic data-screening framework and preprocessing pipeline to support ML-based prognostic surveillance |
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